Characterizing and identifying biological structure

ABSTRACT

Embodiments described relate to techniques for identifying and characterizing biological structures using machine learning techniques. These techniques may be employed to enable a device to identify the particular type of tissue and/or cells (e.g., platelets, smooth muscle cells, or endothelial cells) in, for example, a biological structure, which may be a tissue or a lesion of a duct (e.g., vasculature) in an animal (e.g., a human or non-human animal), among other structures. The machine learning techniques may use raw impedance spectroscopy measurement data in addition to values derived from that raw data. In addition, the machine learning techniques may be used to select frequencies at which to measure impedance and select features to extract from the measured impedance at the selected frequencies to arrive at a small set of frequencies that allow for reliable differentiation.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application Ser. No. 62/424,693, filed Nov. 21, 2016,and titled “CHARACTERIZING AND IDENTIFYING BIOLOGICAL STRUCTURE,” thecontents of which are incorporated herein in their entirety.

BACKGROUND

Blockages of blood vessels (including veins or arteries) may occur invarious parts of an animal (e.g., a human or a non-human animal) and mayhave significant repercussions. In an ischemic stroke, for example, ablood clot fully or partially blocks blood flow in a cerebral artery. Ifthe clot is not treated quickly, insufficient blood flow may causeirreparable damage to the brain.

Blockages may be caused by blood clots, which may be caused bycoagulation of red and/or white blood cells and/or platelets within ablood vessel. The coagulation may be triggered by a variety of factors,including an injury, abnormal blood flow at the site of the blockage, adisease/condition predisposing an animal to coagulation, and/or otherfactors.

A common treatment of a clot is chemical dissolution of the clot, whichis feasible within the first 4.5 hours following blockage of a bloodvessel. Another common option is mechanical thrombectomy, in which anaspiration catheter or a stent-retriever is used to remove the bloodclot from the blood vessel.

Stent-retrievers include a stent attached at the end of a wire. Thestent is deployed into the vasculature and into the clot, expanded intothe clot, and, after a typical waiting time of 0.5 to 10 minutes,extracted to pull the clot out of the blood vessel. Due to non-optimalgrabbing of the clot by the stent-retriever, some parts of the clot maybe left by or be lost from the retriever, such that several successiontreatments (an average 3 times) may be necessary to treat the blockageand restore circulation in the vessel. Each repetition increases injuryto the vessel wall and increases both the intervention duration and theduration of impeded blood flow due to the blockage, potentially leadingto irreparable damage of the animal. The physio-mechanical process ofclot grabbing is currently poorly understood, but the two most commonexplanations for non-optimal grabbing of a clot are (1) thestent-retriever never deploys into the blood clot and only frictioninduced by the stent-retriever pushing the blood clot against the wallis responsible for the retrieval of the clot, and (2) the stent deploysinto the blood clot but an insufficient amount of time was provided forthe stent to coalesce with the blood clot.

If an aspiration catheter is used to remove the blood clot, a clinicianinserts the catheter into the vasculature and operates the catheter toaspirate the clot into the catheter. Depending on the diameter of thecatheter, it may be placed in direct contact with the clot or placed ina proximal region of the vessel. Depending on the composition andviscosity of the clot, the aspiration method may differ. Somedifficulties may arise with aspiration catheters. For example once theclot is aspirated into the catheter, it can block the flow inside thecatheter. In such situations, a clinician may not be aware withoutextraction of the catheter whether the clot is blocking a tip of thecatheter or is inside the catheter and blocking a tube. If the clot isblocking the tip of the catheter, there is a risk the clot may beinadvertently released during removal of the catheter, such that theclot may become an embolism that travels through the blood stream andblocks a vessel in another part of the animal.

SUMMARY

Embodiments described relate to techniques for identifying andcharacterizing biological structures using machine learning techniques.These techniques may be employed to enable a device to identify theparticular type of tissue and/or cells (e.g., platelets, smooth musclecells, or endothelial cells) in, for example, a biological structure,which may be a tissue or a lesion of a duct (e.g., vasculature) in ananimal (e.g., a human or non-human animal), among other structures. Themachine learning techniques may use raw impedance spectroscopymeasurement data in addition to values derived from that raw data. Inaddition, the machine learning techniques may be used to selectfrequencies at which to measure impedance and select features to extractfrom the measured impedance at the selected frequencies to arrive at asmall set of frequencies that allow for reliable differentiation.

In one embodiment, a method of training a system to identify at leastone characteristic of a biological structure is provided. In someembodiments, the method comprises receiving training data including aplurality of sets of impedance measurements for the biologicalstructure, identifying a first subset of the training data including afirst subset of impedance measurements from each set of the plurality ofsets of impedance measurements, identifying a first plurality offeatures from the identified first subset of the training data, thefirst plurality of features including at least one derived feature thatis derived from the identified first subset of the training data, andtraining a model using at least one machine learning technique with thefirst plurality of identified features to create a first trained model.

Embodiments described relate to a medical device including an invasiveprobe that, when inserted into an animal (e.g., a human or non-humananimal, including a human or non-human mammal), may aid in diagnosingand/or treating a lesion (e.g., a growth or deposit within a duct suchas a vasculature that fully or partially blocks the duct). The invasiveprobe may have one or more sensors to sense characteristics of thelesion, including by detecting one or more characteristics of tissuesand/or biological materials of the lesion. The medical device may beconfigured to analyze the characteristics of a lesion and, based on theanalysis, provide treatment recommendations to a clinician. Suchtreatment recommendations may include a manner in which to treat alesion, such as which treatment to use to treat a lesion and/or a mannerin which to use a treatment device. The subject matter of the presentinvention involves, in some cases, interrelated products, alternativesolutions to a particular problem, and/or a plurality of different usesof one or more systems and/or articles.

In one embodiment, there is provided a medical device for diagnosisand/or treatment of a lesion of an animal. The medical device comprisesan invasive probe for insertion into the animal and removal from theanimal following the diagnosis and/or treatment, the invasive probecomprising at least one sensor, at least one processor, and at least onestorage medium having encoded thereon executable instructions that, whenexecuted by at least one processor, cause the at least one processor tocarry out a method. The method comprises identifying a composition ofthe lesion using the at least one sensor, wherein identifying thecomposition of the lesion comprises determining one or more biologicalmaterials present in the lesion and identifying at least onecharacteristic of the lesion based at least in part on the composition.

In certain embodiments, the medical device comprises an invasive probearranged to be inserted into a duct of an animal during diagnosis and/ortreatment of a lesion of the duct and removed from the duct followingthe diagnosis and/or treatment, the invasive probe being configured tomake one or more measurements of the lesion of the duct, the invasiveprobe comprising at least one impedance sensor and at least one circuitto drive the at least one impedance sensor to make a plurality ofmeasurements of impedance of the lesion, wherein each measurement of theplurality of measurements of impedance corresponds to a frequency of aplurality of frequencies and is a measurement of impedance of the lesionwhen an electrical signal of the corresponding frequency is applied tothe lesion.

Certain aspects are related to inventive methods of operating a medicaldevice for diagnosis and/or treatment of a lesion of an animal. Themedical device comprises an invasive probe to be inserted into theanimal and removed from the animal following diagnosis and/or treatmentof the lesion. The method comprises generating, with the invasive probeof the medical device while the invasive probe is disposed within theanimal, a digital signal indicating an impedance spectrum of a pluralityof biological materials of the lesion measured by the invasive probe ata plurality of locations of the lesion, wherein generating the digitalsignal comprises operating the invasive probe to apply an electricalsignal at a plurality of frequencies and operating a plurality ofsensors of the invasive probe to measure impedance of the plurality ofbiological materials of the lesion. The method further comprisesidentifying the lesion based at least in part on an analysis of thedigital signal, determining, using at least one processor of the medicaldevice and based at least in part on an analysis of the digital signaland/or an identity of the lesion, one or more treatment recommendationsfor a manner in which to treat the lesion, and outputting the one ormore treatment recommendations for presentation to a user via a userinterface.

In a further embodiment, there is provided a method of operating amedical device for diagnosis and/or treatment of a lesion of an animal,the medical device comprising an invasive probe to be inserted into theanimal and removed from the animal following the diagnosis and/ortreatment of the lesion. The method comprises generating, with theinvasive probe of the medical device while the invasive probe isdisposed within the animal, data indicating one or more electricalproperties of biological materials present in the lesion of the animal,wherein generating the data comprises operating at least one sensor ofthe invasive probe to measure the one or more electrical properties ofthe biological materials present in the lesion, and outputtinginformation indicative of the one or more electrical properties, forpresentation to a user via a user interface.

In some embodiments, an apparatus is described. In accordance withcertain embodiments, the apparatus comprises at least one processor andat least one storage medium having encoded thereon executableinstructions that, when executed by the at least one processor, causethe at least one processor to carry out a method comprising receiving,over time and from a plurality of medical devices, a plurality ofreports on medical treatments performed on a plurality of lesions ofducts of animals, each report of the plurality of reports comprising oneor more characteristics of a lesion treated in a corresponding medicaltreatments, one or more parameters of the corresponding medicaltreatment performed to treat the lesion, and an indication of outcomefor the corresponding medical treatment; learning, over time and basedon the plurality of reports on medical treatments, one or morerelationships between characteristics of lesions and parameters ofsuccessful and/or unsuccessful treatments of lesions, wherein learningthe one or more relationships comprises determining one or moreconditions to associate with each treatment option of a plurality oftreatment options, wherein the one or more conditions are related tocharacteristics of lesions such that, when characteristics of a lesionsatisfy the one or more conditions for a corresponding treatment option,the corresponding treatment option is to be recommended for treatment ofthe lesion; and configuring the plurality of medical devices to makerecommendations to clinicians from among the plurality of treatmentoptions based on an evaluation of characteristics of lesions withrespect to the one or more conditions associated with each of theplurality of treatment options.

At least one storage medium having encoded thereon executableinstructions that, when executed by at least one processor, cause the atleast one processor to carry out a method is described in accordancewith certain embodiments. In some embodiments, the method comprisesreceiving, over time and from a plurality of medical devices, aplurality of reports on medical treatments performed on a plurality oflesions of ducts of animals, each report of the plurality of reportscomprising one or more characteristics of a lesion treated in acorresponding medical treatments, one or more parameters of thecorresponding medical treatment performed to treat the lesion, and anindication of outcome for the corresponding medical treatment; learning,over time and based on the plurality of reports on medical treatments,one or more relationships between characteristics of lesions andparameters of successful and/or unsuccessful treatments of lesions,wherein learning the one or more relationships comprises determining oneor more conditions to associate with each treatment option of aplurality of treatment options, wherein the one or more conditions arerelated to characteristics of lesions such that, when characteristics ofa lesion satisfy the one or more conditions for a correspondingtreatment option, the corresponding treatment option is to berecommended for treatment of the lesion; and configuring the pluralityof medical devices to make recommendations to clinicians from among theplurality of treatment options based on an evaluation of characteristicsof lesions with respect to the one or more conditions associated witheach of the plurality of treatment options.

Certain embodiments describe a method comprising operating at least oneprocessor to carry out acts of: receiving, over time and from aplurality of medical devices, a plurality of reports on medicaltreatments performed on a plurality of lesions of ducts of animals, eachreport of the plurality of reports comprising one or morecharacteristics of a lesion treated in a corresponding medicaltreatments, one or more parameters of the corresponding medicaltreatment performed to treat the lesion, and an indication of outcomefor the corresponding medical treatment; learning, over time and basedon application of a machine learning process to the plurality of reportson medical treatments, one or more relationships between characteristicsof lesions and parameters of successful and/or unsuccessful treatmentsof lesions, wherein learning the one or more relationships comprisesdetermining one or more conditions to associate with each treatmentoption of a plurality of treatment options, wherein the one or moreconditions are related to characteristics of lesions such that, whencharacteristics of a lesion satisfy the one or more conditions for acorresponding treatment option, the corresponding treatment option is tobe recommended for treatment of the lesion; and configuring theplurality of medical devices to make recommendations to clinicians fromamong the plurality of treatment options based on an evaluation ofcharacteristics of lesions with respect to the one or more conditionsassociated with each of the plurality of treatment options.

In a further embodiment, there is provided a method of diagnosing and/ortreating a lesion of an animal, the method comprising inserting into theanimal an invasive probe of a medical device, the invasive probecomprising at least one sensor to measure one or more characteristics ofeach of a plurality of biological materials of the lesion, identifyingthe lesion based at least in part on the one or more characteristics ofeach of the plurality of biological materials of the lesion, operatingthe medical device to generate one or more recommendations on treatmentof the lesion based at least in part on the one or more characteristicsof each of the plurality of biological materials and/or an identity ofthe lesion, treating the lesion in accordance with the one or morerecommendations of the medical device on treatment of the lesion, andremoving the invasive probe from the duct of the animal.

According to some embodiments, a medical device configured to diagnoseand/or treat a lesion of a duct of an animal is described. In certainembodiments, the medical device comprises inserting an invasive probe ofthe medical device into the duct of the animal, the invasive probecomprising at least one sensor to configured to measure one or morecharacteristics of a tissue and/or biological material of the lesion;further configured to generate one or more recommendations on treatmentof the lesion based at least in part on a measurement of the one or morecharacteristics by the at least one sensor of the invasive probe; andfurther configured to deliver a treatment to the lesion in accordancewith the one or more recommendations on treatment of the lesion. Incertain embodiments, the medical device is also configured to remove thelesion from the duct of the animal.

In one embodiment, there is provided a method of training a system toidentify at least one characteristic of a biological structure. Themethod comprises receiving training data including a plurality of setsof impedance measurements for the biological structure, identifying afirst subset of the training data including a first subset of impedancemeasurements from each set of the plurality of sets of impedancemeasurements, identifying a first plurality of features from theidentified first subset of the training data, the first plurality offeatures including at least one derived feature that is derived from theidentified first subset of the training data, and training a model usingat least one machine learning technique with the first plurality ofidentified features to create a first trained model.

In another embodiment, there is provided a method of training a systemto identify at least one characteristic of a biological structure. Themethod comprises operating at least one processor to carry out an act ofselecting a subset of impedance measurements from each of a plurality ofsets of impedance measurements for biological structures to produce aplurality of subsets of impedance measurements. Each set of impedancemeasurements comprises measurements of impedance of one of thebiological structures in response to applications of signals ofdifferent frequencies. The method further comprises operating the atleast one processor to carry out an act of generating a plurality ofsets of features. Each set of features characterizes a subset ofimpedance measurements of the plurality of subsets. Each set of featuresincludes at least one feature present in a subset of impedancemeasurements and at least one derived feature that is derived from asubset of impedance measurements. The method further comprises operatingthe at least one processor to carry out an act of training a model torecognize the at least one characteristic of a target biologicalstructure based on input impedance measurements for the targetbiological structure. The training comprises applying at least onemachine learning technique to the plurality of sets of features thatcharacterize the plurality of subsets of impedance measurements tocreate a trained model.

In a further embodiment, there is provided a method of training a systemto identify at least one characteristic of a biological structure. Themethod comprises operating at least one processor to carry out an act oftraining a first model using a plurality of sets of impedancemeasurements and, for each set of impedance measurements, an indicationof a biological structure to which the set of impedance measurementscorresponds. The plurality of sets of impedance measurements includeimpedance measurements for a plurality of types of biologicalstructures. The training comprises training the first model, based atleast in part on the impedance measurements, to differentiate impedancemeasurements for a first type of biological structure from impedancemeasurements for one or more other types of biological structures. Thetraining comprises applying at least one machine learning technique. Themethod further comprises operating the at least one processor to carryout an act of training a second model, at least in part by applying atleast one machine learning technique to impedance measurements for thefirst type of biological structure, to identify the at least onecharacteristic of the first type of biological material.

In another embodiment, there is provided a method of determining atleast one characteristic of a biological structure. The method comprisesoperating at least one processor to carry out an act of evaluatingimpedance measurements for the biological structure using at least onetrained model to determine the at least one characteristic. The at leastone trained model is trained to distinguish between biologicalstructures having different characteristics.

In a further embodiment, there is provided a method of determining amanner in which to treat a lesion of an animal. The method comprisingoperating at least one processor to carry out an act of evaluatingimpedance measurements for the lesion using at least one trained modelto determine the manner in which to treat the lesion. Evaluating theimpedance measurements comprises evaluating one or more features of theimpedance measurements using the at least one trained model. The one ormore features comprise at least one derived feature that is derived fromthe impedance measurements.

In another embodiment, there is provided a method of training a systemto identify a manner in which to treat a biological structure. Themethod comprises receiving training data including a plurality of setsof impedance measurements for the biological structure, identifying afirst subset of the training data including a first subset of impedancemeasurements from each set of the plurality of sets of impedancemeasurements, identifying a first plurality of features from theidentified first subset of the training data, the first plurality offeatures including at least one derived feature that is derived from theidentified first subset of the training data, and training a model usingat least one machine learning technique with the first plurality ofidentified features to create a first trained model.

In a further embodiment, there is provided a method of training a systemto identify a manner in which to treat a biological structure. Themethod comprises operating at least one processor to carry out an act ofselecting a subset of impedance measurements from each of a plurality ofsets of impedance measurements for biological structures to produce aplurality of subsets of impedance measurements. Each set of impedancemeasurements comprising measurements of impedance of one of thebiological structures in response to applications of signals ofdifferent frequencies. The method further comprises operating the atleast one processor to carry out an act of generating a plurality ofsets of features. Each set of features characterizes a subset ofimpedance measurements of the plurality of subsets. Each set of featuresincludes at least one feature present in a subset of impedancemeasurements and at least one derived feature that is derived from asubset of impedance measurements. The method further comprises operatingthe at least one processor to carry out an act of training a model todetermine, from input impedance measurements for the target biologicalstructure, a treatment to recommend for a target biological structurefrom among a plurality of treatment options. The training comprisesapplying at least one machine learning technique to the plurality ofsets of features that characterize the plurality of subsets of impedancemeasurements to create a trained model.

In another embodiment, there is provided a method of training a systemto identify a manner in which to treat a biological structure, themethod comprising operating at least one processor to carry out an actof training a first model using a plurality of sets of impedancemeasurements and, for each set of impedance measurements, an indicationof a biological structure to which the set of impedance measurementscorresponds. The plurality of sets of impedance measurements includeimpedance measurements for a plurality of types of biologicalstructures. The training comprises training the first model, based atleast in part on the impedance measurements, to differentiate impedancemeasurements for a first type of biological structure from impedancemeasurements for one or more other types of biological structures. Thetraining comprising applying at least one machine learning technique.The method further comprises operating the at least one processor tocarry out an act of training a second model, at least in part byapplying at least one machine learning technique to impedancemeasurements for the first type of biological structure, to determine atreatment to recommend for a target biological structure that is of thefirst type, from among a plurality of treatment options.

In a further embodiment, there is provided at least one storage mediumhaving encoded thereon executable instructions that, when executed by atleast one processor, cause the at least one processor to carry out anyof the foregoing methods.

In another embodiment, there is provided an apparatus comprising atleast one processor and at least one storage medium having encodedthereon executable instructions that, when executed by the at least oneprocessor, cause the at least one processor to carry out any of theforegoing methods.

Other advantages and novel features of the present invention will becomeapparent from the following detailed description of various non-limitingembodiments of the invention when considered in conjunction with theaccompanying figures. In cases where the present specification and adocument incorporated by reference include conflicting and/orinconsistent disclosure, the present specification shall control. Theforegoing is thus a non-limiting summary of the invention, which isdefined by the attached claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a flowchart of a manner in which a clinician may operate amedical device to diagnose and/or treat a lesion, in accordance withembodiments described herein;

FIG. 2 is an illustration of an example of a medical device inaccordance with some embodiments;

FIG. 3 is an illustration of an example of an invasive probe inaccordance with some embodiments;

FIGS. 4-5 are flowcharts of processes that may be implemented in someembodiments to determine a composition of a lesion;

FIG. 6 is a representation of an exemplary frequency spectrum of themodulus of the impedance of a lesion;

FIGS. 7-10 illustrate exemplary models of the impedance of a lesion,that may be implemented in the method of FIG. 4 , including a constantphase element;

FIG. 11 illustrates an exemplary system for implementing the method ofFIG. 4 ;

FIG. 12 is a flowchart of an illustrative method for operation ofmedical devices in accordance with some embodiments described herein togenerate treatment recommendations;

FIG. 13 is a flowchart of another illustrative method of someembodiments for operation of medical device in accordance withembodiments described herein to generate treatment recommendations basedin part on a composition of a lesion;

FIG. 14 is a flowchart of an illustrative manner of generating treatmentrecommendations using conditions, which may be implemented in someembodiments;

FIGS. 15A-15B are flowcharts of illustrative processes for operating aserver to analyze reports on treatments to determine conditions withwhich to configure medical devices, which may be implemented in someembodiments;

FIG. 15C is a flowchart of an illustrative process for operating aserver to learn relationships between raw data and options forsuccessful treatments, which may be implemented in some embodiments;

FIG. 16 is an example of a process that may be implemented in someembodiments for generating a chronicle of a treatment;

FIG. 17A is an example of a process that may be implemented in someembodiments for training a model to accurately characterize lesionsand/or providing treatment recommendations;

FIG. 17B is an example of a process that may be implemented in someembodiments for generating a filter using a machine learning model;

FIG. 17C is an example of a process that may be used in some embodimentsto aid a clinician in guiding an invasive probe;

FIG. 18 shows an example, in diagram form, of effective capacitances ofcellular structures determined by the method of FIG. 4 ;

FIGS. 19 and 20 show examples of systems made in accordance with aspectsof the present disclosure;

FIG. 21A is a histogram showing the determined effective capacitance ofmultiple types of cells under controlled conditions;

FIG. 21B is a histogram showing the determined effective capacitance ofmultiple types of cells under uncontrolled conditions;

FIG. 22 is a flowchart of an illustrative process for training a modelusing machine learning techniques to analyze a biological material;

FIG. 23 is an example confusion matrix resulting from the application oftraining data to a trained model created by the process shown in FIG. 22;

FIG. 24-24A are examples of confusion matrices resulting from theapplication of test data to a trained model created by the process shownin FIG. 22 ;

FIG. 25A-25B are graphs showing amplitude and phase spectra forexperimental data;

FIGS. 26A-27F are histograms showing various parameter distributions;and

FIGS. 28-30 are histograms showing distributions of valuesrepresentative of effective capacitance for different cell types;

FIG. 31-33 are flowcharts of illustrative methods of some embodimentsfor operation of medical devices in accordance with embodimentsdescribed herein to generate treatment recommendations based in part onthe characteristics of cancerous and/or noncancerous tissue; and

FIG. 34 is a block diagram of a computing device with which someembodiments may operate.

The application file of U.S. Provisional application 62/424,693, towhich the present application claims priority, contains at least one ofthe above drawings executed in color. Copies of the color drawing(s)will be provided by the U.S. Patent and Trademark Office upon requestand payment of the necessary fee.

DETAILED DESCRIPTION

Some embodiments described herein relate to a medical device includingan invasive probe that, when implanted or inserted into an animal (e.g.,a human or non-human animal, including a human or non-human mammal), mayaid in diagnosing and/or treating a lesion of the animal. The lesion maybe an abnormality in the biological structure of the animal, such as adeviation from a normal structure and/or function of a part of ananimal, such as an abnormality associated with injury, a medicalcondition, or a disease. The lesion may appear in different parts of theanimal, for example it may be included within a duct of the animal Alesion of a duct may, for example, act as a blockage that fully orpartially blocks the duct. The duct may be, for example, a blood vesselof the animal or other duct and the lesion may be formed, in whole or inpart, by a growth in the duct, an accumulation of material in the duct,and/or any other cause of a lesion. The invasive probe may have one ormore sensors to sense characteristics of the lesion, which may includedetecting a composition of the lesion.

In some embodiments, detecting a composition of a lesion may includeidentifying one or more biological materials of the lesion, includingone or more cells and/or one or more tissues present in the lesion,and/or one or more plaque materials present in the lesion. Thebiological materials of the lesion that are identified may be allbiological materials present in the lesion, or only some of thebiological materials present in the lesion. Where only some of thebiological materials are identified, the identified material(s) may beonly those materials of a certain type of material, such astissues/cells of the lesion (as compared to other materials, such asplaque materials) or a particular type of tissues/cells (e.g., red bloodcells present in the lesion, and not other types of cells). In cases inwhich a composition is determined and in which only one or some type(s)of biological materials are identified, determining the composition mayinclude determining the amount(s) of the identified material(s) in thelesion, such as determining the amount(s) of the identified material(s)relative to the total materials of the lesion, including by calculatinga ratio of one or more of the identified material(s) to the totalmaterials of the lesion.

In some such embodiments, the invasive probe may identify and/orcategorize the lesion. Identifying or categorizing the lesion mayinclude, in some embodiments, diagnosing the lesion. The medical devicemay be configured to analyze the lesion and, based on the analysis,provide treatment recommendations to a clinician. Such treatmentrecommendations may include a manner in which to treat the lesion, suchas which treatment or combination of treatments to use to treat thelesion (e.g., if the lesion is to be removed, whether to use anaspiration catheter or a stent-retriever) and/or a manner in which touse a treatment device (e.g., how fast to extract a stent-retriever). Insome such embodiments, the invasive probe may perform such identifyingor categorizing, and/or generate such treatment recommendations, basedat least in part on the composition of the lesion (e.g., the identity ofone or more biological materials of the lesion) and/or one or more othercharacteristics of the lesion as a whole. For example, a systemincluding the invasive probe and/or other associated devices describedbelow may identify biological materials of a lesion and, based on theidentified materials, identify the type of or otherwise categorize thelesion. Based on the identification of the lesion, the system maygenerate recommendations for treatment of the particular type of lesion.As another example, in other embodiments, the system may identifybiological materials of a lesion and, based on the identified materials(alone, or relative to other material(s) of the lesion), generaterecommendations for treatment of the lesion. As another example, inother embodiments, the system may, based on information characterizingthe biological materials (e.g., impedance spectra) of the lesion,generate recommendations for treatment of the lesion.

Embodiments additionally or alternatively relate to techniques foridentifying and characterizing biological structures using machinelearning techniques. These techniques may be employed to enable a deviceto identify the particular type of tissue and/or cells (e.g., platelets,smooth muscle cells, or endothelial cells) in, for example, a biologicalstructure, which may be a tissue or a lesion of a duct (e.g.,vasculature) in an animal (e.g., a human or non-human animal), amongother structures. The machine learning techniques may use raw impedancespectroscopy measurement data in addition to values derived from thatraw data. In addition, the machine learning techniques may be used toselect frequencies at which to measure impedance, to arrive at a smallset of frequencies that allow for reliable differentiation.

In some embodiments, the invasive probe may include one or a number ofsensors, which may include sensors to measure an impedance of thelesion. The sensors may measure impedance of the lesion when electricalsignals having particular frequencies are applied to the lesion. Themedical device may be configured to, based on the impedance values,determine a composition of the lesion and/or one or more characteristicsof the lesion. For example, each sensor may, in some embodiments, beoperated to detect an impedance spectrum of a biological materialcontacting the sensor, such that different sensors of the invasive probemay, at the same time, generate different impedance spectra fordifferent biological materials of the lesion. The medical device maythen generate the treatment recommendations based in part on thedetermined composition. As discussed above, determining the compositionmay include identifying amounts of one or more biological materialswithin the lesion, which may be less than all materials of the lesion.For example, in some embodiments, an amount of the lesion that iscomposed of red blood cells is determined.

Various examples described herein will discuss the medical device incontext of vasculature lesions and manners of treating vasculaturelesions. It should be appreciated, however, that embodiments are not solimited. Techniques described herein for sensing characteristics oflesions and generating treatment recommendations may be used with anysuitable lesion, including with any suitable lesion of an anatomicalduct of an animal or lesions that may appear in other locations withinan anatomy of an animal. In the case that a lesion is a lesion of aduct, such ducts may include vasculature ducts and gastrointestinalducts, for example. Those skilled in the art will appreciate that ductsin anatomy differ from anatomical cavities. For example, a duct may besignificantly smaller in one dimension (e.g., a width) than in anotherdimension (e.g., a length).

Thus, in some embodiments, the invasive probe may be a component of amedical device for diagnosis and/or treatment of a lesion ofvasculature. For example, the medical device may be a thrombectomydevice and the invasive probe may be a component of the thrombectomydevice. Accordingly, the invasive probe may be a component of a guidewire, an aspiration catheter, a micro-catheter, a stent-retriever,and/or another thrombectomy device. In some embodiments, a medicaldevice may include two or more of a guide wire, an aspiration catheter,and a stent-retriever and the invasive device may be a component of oneor more of these, including all of these.

The inventors have recognized and appreciated that typical conventionaltechniques for identifying a lesion, including identifying a type oflesion in a duct, based on electrical measurements of a lesion do nothave sufficient accuracy or reliability to be effectively used in amedical setting. Some such conventional techniques involve generation ofa large number of impedance spectra for whole lesions, for each of avariety of types of lesions, and generating an “average” impedancespectrum for each type of lesion. However, lesions vary greatly fromperson-to-person, or even within the same person, making it impracticalto generate an accurate or representative “average” or “standard”impedance spectrum for a lesion as a whole. Other such conventionaltechniques attempt to improve reliability by imposing a rigorousmeasurement process, requiring a precise positioning of sensorscontacting a lesion for each measurement during determination of a“standard” spectrum, and the same measurement position during subsequentuse on a patient. Such precise positioning is nearly impossible toreplicate and reproduce in practice, and still does not improvereliability to the point that these techniques can be used with asufficient degree of accuracy to be helpful for use with patients. Forexample, during use with a patient, a measurement of an impedancespectrum of the lesion of the patient would need to be performed,measurement would need to be compared against multiple “standard”spectra for each type of lesion, and computationally-intensivestatistical analyses would need to be performed to identify the type oflesion. However, for typical conventional technologies, even thesecomplex analyses yield results with degrees of confidence of onlyslightly above 50%, at best.

Some embodiments described herein are directed to methods foridentifying the type of a lesion, which do not involve the use ofdatabases of “standard” impedance spectra for lesions as a whole, orstatistical analyses to compare such whole-lesion impedance spectra.Some of these embodiments are configured to characterize lesions byidentifying a composition of the lesion, such as the type and number ofsome or all of the biological materials that are present in the lesion.This may include identifying one or more tissues and/or cells of thelesion, and/or one or more plaque materials present in the lesion. Insome such embodiments, the composition of the lesion may then beanalyzed to identify characteristics of the lesion with high degrees ofconfidence. Such characteristics of the lesion may include a type of thelesion, and the embodiment may include diagnosing the lesion. In someembodiments, the composition of the lesion may be compared to one ormore conditions that are associated with types of lesions, such asconditions identifying that a particular type of lesion is associatedwith a particular composition (e.g., a particular set of biologicalmaterials, or particular relative amounts of biological materials). Oncea particular composition is determined to have matched a type of lesionby satisfying the condition(s) associated with the type, the lesionhaving that composition may be identified as being of that type.Identifying the lesion based on an identification of biologicalmaterials of the lesion may have high reliability (e.g., greater than90%).

The inventors have further recognized and appreciated that conventionalmedical devices, including conventional thrombectomy devices, do notprovide information on characteristics of lesions of vasculatureincluding blood vessels, nor do the conventional medical devices provideinformation on status of treatment of a lesion. The inventors haveadditionally recognized and appreciated that this lack of informationcontributes to difficulties of treating lesions. For example, withoutinformation on a composition of a lesion, a clinician may havedifficulty selecting between available treatment options, as eachtreatment option may work best for lesions of different compositions.Moreover, without information on a status of a treatment for a lesion,the clinician may not be aware of whether a treatment is beingsuccessfully or unsuccessfully performed. Because of this lack ofinformation, multiple treatments may be necessary to correctly treat alesion. Each such treatment increases risk of injury to a patient and,more importantly for some lesions, increases the duration of lesion.When a vessel is partially or fully blocked by a lesion, the decreasedblood flow may cause injury to tissues of the animal.

Accordingly, in accordance with embodiments described herein, a medicaldevice may determine characteristics of a lesion and monitor performanceof a treatment, as well as generate recommendations on a manner in whichto treat a lesion before and/or during the treatment. This additionalinformation may aid a clinician in initially determining how to treat alesion, as well as in performing the treatment to try to ensure that, orat least increase a chance that, a lesion is removed with only onetreatment and that subsequent treatments are not needed for the samelesion. The medical device may provide information to the clinician inreal-time, during a medical intervention, such as by providing real-timeinformation to the clinician on interactions between the medical deviceand the lesion. Real-time may, in some embodiments, include providinginformation to the clinician within a time period of corresponding databeing sensed by the medical device, where the time period may be lessthan 5 seconds, less than seconds, less than 30 seconds, less than oneminute, or less than 5 minutes, which may be dependent on requirementsof an analysis to be performed on data to generate recommendations.

In some embodiments, reliability and effectiveness of techniques anddevices may be improved through initialization and configuration,including by configuring a system using a particular approach toselection of data points to be collected, generated, and/or used for abiological structure to be used in characterizing materials of thestructure or the structure itself, and/or in generating treatmentrecommendations. The determination of such data points to be collectedmay include a determination of frequencies at which to measure animpedance spectrum of biological materials. The determination mayadditionally or alternatively include determining, once impedance valuesfor frequencies (e.g., a range of frequencies, or any set of selectedfrequencies) are collected, what features of those impedance values areto be used in subsequent analysis, including identification of whichexplicit data values within the set of impedance values are to be usedor what values are to be derived from an analysis of the data values,for use in subsequent analysis. Some embodiments may include identifyingsuch frequencies and/or features using a machine learning analysis.

Features with which embodiments may operate include descriptors orattributes of data or sets of data, including descriptors or attributesof sets of impedance measurements. A descriptor or attribute of ameasurement or a set of measurements may characterize a data point in adata set or the set of data. A feature may have a value, such as anumeric value. When a feature is used to characterize differentmeasurements or data sets of measurements, the feature may be the samedescriptor or attribute for the different measurements/sets and thuscharacterize the measurements/sets in the same or a similar way, but mayhave different values that correspond to the data points of thosedifferent measurements/sets.

Features of the types described herein may include features that arepresent in a measurement of impedance or a set of measurements ofimpedance (e.g., an Electrical Impedance Spectroscopy measurements)and/or features derived from an impedance measurement or set. Featurespresent in from measurements of impedance may include numeric valuesthat are explicitly set out within a measurement or a measurement dataset. Examples of such features include a magnitude or phase of animpedance measurement or, from among a set of impedance measurements, aminimum or maximum numeric value in the set (where a minimum/maximum maybe absolute and/or relative). The minimum or maximum data value mayrequire some analysis, such as a comparison of values, but theminimum/maximum value itself will be a value found within the data set.Derived features may describe a measurement or set, but include a valuenot found in the measurement/set. Instead, a value of a derived featuremay be derived from the measurement/set, such as obtained throughperforming one or more computations on the measurement/set. Examples ofderived features include an average value of the EIS measurements, aphase maximum frequency of the EIS measurements, an n-quantile of theEIS measurements, a first derivative of the EIS measurements, and asecond derivative of the EIS measurements, among other.

In some embodiment, the initialization and configuration may includetraining a filter to distinguish between impedance measurements and/orfeatures that relate to a biological structure of interest, such as aparticular type of tissue or a particular type of lesion, and otherbiological structures. In some scenarios, when impedance measurementsare collected for a particular type of lesion, tissue, or otherbiological structure, one or more of the impedance measurements that arecollected may correspond to another biological structure. For example,in some cases a probe that is used to collect impedance measurements fora blood clot or other lesion of a blood vessel may not contact only theblood clot, but may also contact other tissues proximate to the bloodclot, such as a vessel wall. These impedance measurements for othertissues or biological structures may hinder proper analysis of the bloodclot. The inventors have recognized and appreciated the advantages oftraining a model, using one or more machine learning techniques, todistinguish between impedance measurements for a biological structure ofinterest and impedance measurements reflective of other biologicalstructures or reflective of an error in data collection. When a systemis to be trained to recognize characteristics of a lesion (or otherbiological structure of interest) in a part of a body of an animal, oneor more other biological structures that may be found in that part ofthe body or otherwise would be proximate to the lesion may beidentified. Impedance measurements may be collected for those otherbiological structures. Once collected, the impedance measurements may beused, alongside other impedance measurements for the lesion (or otherbiological structure of interest), to train a model to distinguishbetween impedance measurements that are for the lesion (or otherbiological structure of interest) or are for other structures. Oncetrained, the model may be used to filter input impedance measurements,to prevent or mitigate the chances of impedance measurements for anotherbiological structure interfering with analysis of impedance measurementsfor the lesion.

It should be appreciated that not all lesions are formed within ducts,and that some embodiments may operate with lesions disposed in areas ofthe body other than ducts. For example, some cancerous cells may beformed on other parts of an animal (e.g., a human) body. Someembodiments described herein relate to diagnosis and/or treatment oflesions, such as cancerous cells, that are not typically found withinducts. It should be appreciated, however, that some cancerous cells maybe found within ducts, and other embodiment described herein relate todiagnosis and/or treatment of such cancerous cells.

It should also be appreciated that while some examples described belowrelate to lesions, embodiments are not limited to operating with lesionsand may operate with any biological structure of interest, having anysuitable composition of biological materials.

GENERAL DISCUSSION OF TECHNIQUES

To provide context for a discussion of exemplary components of a medicaldevice operating in accordance with some embodiments described herein,FIG. 1 is a flowchart of a process that may be followed by a clinicianto operate such a medical device. FIGS. 2-3 illustrate examples of amedical device, while other figures below detail other components of adevice and ways in which such a device may be operated.

The process 100 may be used to diagnose and/or treat a lesion in subjectthat is an animal. The animal may be, for example, a human or anon-human animal, including a human or non-human mammal. The lesion maybe a lesion within a duct, such as within a blood vessel like a vein orartery of the animal. A duct lesion may be fully or partially blockingthe duct. Embodiments described herein may operate with lesions ofdifferent characteristics, such as:

-   -   in vasculature, a blood clot (including red blood cells, white        blood cells, fibrins, thrombi, emboli, and/or platelets) that        formed at the site of the lesion or formed elsewhere in the body        and became stuck at the site of the lesion;    -   a growth from the duct wall toward a center of the duct, such as        a growth of scar tissue following an injury to endothelial cells        at the site of the lesion or other growth;    -   tissue (e.g., smooth muscle cells, elastic fibers, external        elastic membrane, internal elastic member, loose connective        tissues, and/or endothelial cells) otherwise extending from a        wall of the duct toward a center of the duct that is not        anatomically “normal” or “healthy” for that duct at that site;    -   an accumulation of plaque materials at the site of the lesion,        including an accumulation of cholesterol, calcium, fatty        substances, cellular waste products, fibrin, and/or other        materials that may be found within fluids flowing through a duct        of an animal (e.g., substances found within blood of an animal,        in the case of a vasculature lesion);    -   cancerous cells found in ducts such as metastases and/or        lymphomas; and/or    -   any other tissues and/or biological materials that may cause a        lesion of a duct of an animal.

Lesions of different characteristics may be formed outside ducts. Theselesions include cancerous cells such as carcinomas, myelomas, leukemia,lymphoma, melanomas, neoplasms, mixed type and/or sarcomas.

In some embodiments, the histology of a lesion (e.g., which of thebiological materials listed above the lesion possesses) may bedetermined through identifying, based on a plurality of impedancespectra for the lesion, a composition of a lesion, where the compositionmay indicate biological materials present in the lesion. Such anidentification of biological tissues may include identifying tissuesand/or cells that are present in the lesion, and/or plaque materialspresent in the lesion, and/or the relative amounts of such tissues,cells, or plaque materials in the lesion. In some embodiments,identifying the biological materials present in the lesion may includeidentifying a state of each biological material, such as, fortissues/cells, whether the tissues/cells are healthy or unhealthy. Anunhealthy state of a cell may include, for example, whether the cell isinflamed, diseased, cancerous, or otherwise in an abnormal state.

It should be appreciated that embodiments are not limited to operatingwith lesions of any particular form or composition, or at any particularlocations within an anatomy of a subject. As mentioned above, for easeof description, various examples will be provided below in which theduct is vasculature of an animal.

Prior to the start of process 100 of FIG. 1 , the subject may exhibitsymptoms of a vasculature lesion. An initial determination may be madeby a clinician of whether there is a lesion and a potential location ofthe lesion, such as using imaging techniques like angiography. Based onthe symptoms and the initial determination of a location of a lesion, aclinician may choose to insert an invasive device into vasculature ofthe subject to further diagnose and/or treat the lesion. The clinicianmay be, for example, a doctor (e.g., a physician or surgeon) or may beanother medical professional such as a nurse or medical technicianoperating the medical device (potentially under a doctor's oversight).In some embodiments, the clinician may be located in the same room asthe subject, including next to the subject, while in other embodimentsthe clinician may be located remote from the subject (e.g., in adifferent room of the same building as the patient, or geographicallyremote from the patient) and operating a user interface that controlsthe medical device via one or more wired and/or wireless networks,including the Internet or other wide area network (WAN).

The process 100 begins in block 102, in which a clinician inserts aninvasive probe into vasculature of the subject. The invasive probeinserted by the clinician in block 102 may be located at a distal end ofa guide wire for the medical device, and may be shaped, sized, andarranged for insertion into vasculature. In addition, in block 102, theclinician may feed the invasive probe through the subject's vasculatureuntil the invasive probe is located proximate to the lesion. To do so,the clinician may monitor a position of the invasive probe within asubject using imaging techniques, such as using angiography techniques.The insertion and feeding of the invasive probe in block 102 may beperformed using suitable techniques for insertion of devices intovasculature, including using known techniques, as embodiments are notlimited in this manner.

In block 104, the clinician operates the invasive probe to determine oneor more characteristics of the lesion. A characteristic may include aphenotype and/or genotype of a biological structure like a lesion,including a property that distinguishes between biological structures ordistinguishes between phenotypes of biological structures. Acharacteristic may be a property that impacts treatment of the lesion(or other biological structure), as lesions having the property may betreated from lesions not having the property, or lesions havingdifferent values for the property may be treated differently. Suchproperties may be histological, relating to an anatomy of the lesion,and/or anatomical, relating to how the lesion is positioned in orinteracts with the body of the animal. A characteristic may thereforedescribe a lesion. Illustrative characteristics include a location ofthe lesion, a size of the lesion (e.g., length), a composition of thelesion, or other characteristics discussed in detail below. To determinethe characteristics, one or more sensors of the invasive probe may makeone or more measurements of tissues and/or other biological materials ofthe lesion, and/or of tissues/materials otherwise at the site of thelesion such as healthy tissues disposed near the lesion.

In some embodiments, determining one or more characteristics of thelesion may comprise identifying the composition of the lesion, forexample by identifying the amount of different type of cells or tissuesthat are present in the lesion. As one example, it may be identifiedthat a probed lesion is composed of 50% red blood cells, 30% fibrin and20% platelets.

Examples of sensors and measurements are described in detail below. Tooperate the invasive probe in block 104, the clinician may contact thelesion with the one or more sensors of the invasive probe, and/oroperate a user interface of the medical device to trigger the invasiveprobe to use the sensor(s) to detect the characteristics of the lesion.

In block 106, the clinician operates the medical device to generate andoutput treatment recommendations for the lesion based on the determinedcharacteristic(s) of the lesion. As discussed in detail below, thetreatment recommendation(s) generated by the medical device based on thecharacteristic(s) of the lesion may include recommendations on a mannerin which to treat a lesion, such as which treatment device to use totreat a lesion (e.g., if material of the lesion is to be removed fromthe subject, whether to use an aspiration catheter or a stent-retriever)and/or a manner in which to use a treatment device (e.g., how fast toextract a stent-retriever). As also discussed in detail below, themedical device may generate the treatment recommendations based on avariety of analyses, such as by comparing the characteristic(s) of thelesion to conditions associated with each of multiple differenttreatment options and outputting a recommendation of a treatment optionwhen characteristic(s) of the lesion satisfy corresponding conditionsfor the treatment option. The output by the medical device may be viaany suitable form of user interaction, including a visual, audible,and/or haptic feedback to the clinician via the user interface. In someembodiments, the medical device may in block 106 automatically, withoutfurther user intervention, analyze characteristic(s) of the lesiondetermined in block 104 and generate/output the treatmentrecommendations. In other embodiments, the clinician may operate theuser interface of the medical device to request the analysis andgeneration/output of the treatment recommendations.

In block 108, the clinician considers the treatment recommendations ofthe medical device and selects a treatment option and, in block 110,treats the lesion using the selected treatment option.

In some embodiments, the selected treatment option may include insertionof additional invasive medical components into vasculature of thesubject. If the invasive probe inserted in block 102 was a component ofa guide wire, for example, an additional treatment device may beinserted along the guide wire. As a specific example of such a case, ifthe medical device recommends full or partial removal of the lesionusing a stent-retriever, a stent-retriever may be inserted into thevasculature. As another example, if the medical device recommendsremoval instead with an aspiration catheter, the clinician may insert anaspiration catheter into the vasculature. As a further example, if themedical device recommends implantation of a stent, a stent implanter maybe inserted into the vasculature.

In other embodiments, the treatment may not require insertion of anotherdevice. For example, the invasive probe inserted in block 102 may not bea component of a guide wire, but may instead be a component of treatmentdevice such as a stent-retriever. In such a case, the treatment of block110 may be performed using the treatment device that was inserted inblock 102. For example, if the invasive probe inserted in block 102 is acomponent of a stent retriever, the treatment recommendation of block106 may be specific to a manner of operating a stent-retriever, such asan amount to expand the stent, an amount of time to wait for a clot tocoalesce with the stent, and/or a force or speed with which to withdrawthe stent and clot. In such an embodiment, in block 110, the clinicianmay treat the lesion by operating the stent-retriever as recommended bythe medical device in block 106.

In some embodiments, a treatment recommendation may be generated thatincludes not treating the lesion, but rather treating a subject in amanner that leaves the lesion intact. For example, some types ofvascular lesions may be difficult to effectively treat, preventingrecanalization of the vessel that is occluded by the lesion. Forexample, lesions reflective of intracranial artherosclerotic disease(ICAD lesions) may be difficult to remove given treatments currentlyavailable. In some embodiments, then, until an effective ICAD treatmentis available, if an TCAD lesion is detected, the medical device maygenerate a treatment recommendation that is to not treat the lesion. AnICAD lesion may be identified by its composition and, in particular,location of biological materials within the lesion. For example, if themedical device determines, through techniques described herein, that avascular lesion includes a clot on the luminal side of the invasiveprobe and atherosclerotic plaque materials (e.g., lipidic or calcifiedcomponents, smooth muscle cells, absence of endothelium) on theabluminal side of the probe, the medical device may determine that thelesion is an ICAD lesion.

Once the lesion is treated in block 110, the process 100 ends.Additional actions that may be taken in some embodiments followingtreatment of a lesion are described below.

Examples of Medical Devices

As discussed above, FIG. 1 provided a general discussion of a manner inwhich a medical device may be operated in accordance with someembodiments described herein to diagnose and/or treat a lesion invasculature of an animal. FIGS. 2-3 provide examples of some embodimentsof a medical device that includes an invasive probe that may be insertedinto vasculature as part of such diagnosis and/or treatment.

FIG. 2 illustrates a medical device 200 that may be operated by aclinician 202 to diagnose and/or treat a medical condition of a subject204. The medical condition of the animal 204 (e.g., a human) may be alesion 204A of vasculature, illustrated in the example of FIG. 2 as alesion within a cranial blood vessel of a human, which may cause anischemic stroke. As discussed above, the lesion 204A may be a bloodclot, accumulation of plaque materials, excessive growth of smoothmuscle tissue, and/or other lesion of a blood vessel.

The medical device 200 as illustrated in FIG. 2 includes a guidewire206, a handle 208, and an invasive probe 210. The invasive probe 210 andat least some of the guidewire 206 may be inserted into vasculature ofthe subject 204 until the invasive probe 210 is located proximate to thelesion 204A. The invasive probe 210 may therefore be shaped andotherwise arranged for insertion into the vasculature (or other duct).In some embodiments, the invasive probe 210 will be attached to aguidewire that is approximately 300 micrometers, or a microcatheter thatis approximately 3-10 french in diameter, or another device having adiameter suitable for insertion into a duct of an animal. Such a devicemay be approximately 1 or 2 meters long in some such embodiments, withthe invasive probe 210 located at one end of the guidewire/device, forexample within last centimeters of the device.

The invasive probe 210 that is inserted into the subject 204 may includeone or more sensors 212 and a measurement unit 214. In some embodiments,the sensor(s) 212 may measure one or more electrical characteristics ofthe lesion 204A, including by measuring one or more electricalcharacteristics of tissue and/or biological material of the lesion 204A.The measurement unit 214 may receive data generated by the sensor(s) 212and may, in some embodiments, generate one or more electrical signals tobe applied to the lesion 204A as part of measuring the one or moreelectrical characteristics.

Examples of sensors 212 are described in detail below. As one specificexample, the sensor(s) 212 may be impedance sensors and the measurementunit 214 may drive the sensor(s) 212 to perform Electrical ImpedanceSpectroscopy (EIS) of the lesion 204A. For example, the measurement unit214 may include one or more oscillators to produce electrical signals ofone or more frequencies, which may be specific frequencies that areselected (and that the oscillators of the measurement unit 214 areconfigured to produce) for discriminating between different tissuesand/or different biological materials, to aid in identifying compositionof a lesion 204A, as discussed in detail below. In embodiments that arearranged to test tissues/materials using multiple frequencies, themeasurement unit 214 may include multiple oscillators, one oscillatorbeing specific to each frequency to be tested and being arranged togenerate a signal of that frequency.

In some embodiments in which the measurement unit 214 generateselectrical signals to be applied to the lesion 204A, it may beadvantageous for the measurement unit 214 to be included within theinvasive probe 210 and inserted into the vasculature of the subject 204.This may place the measurement unit 214 in close proximity to thesensors 212 and lesions 204A, and limit noise in electrical signalsapplied to the lesion 204A. If the measurement unit 214 were located inthe handle 208, for example, electrical signals generated by themeasurement unit 214 would travel the length of the guidewire 206 beforebeing output by the invasive probe 210 to be applied to the lesion 204A.If the signals were to travel the length of the guidewire 206,electrical noise may affect signal quality. By positioning themeasurement unit 214 within the invasive probe 210, noise in the signalsmay be limited. When the measurement unit 214 is positioned within theinvasive probe 210, it may be positioned within a lumen of the invasiveprobe 210, on a surface (interior or exterior) of the invasive probe210, or embedded in a film affixed to a surface (interior or exterior)of the invasive probe 210.

The measurement unit 214 may, in some embodiments, be arranged as anApplication Specific Integrated Circuit (ASIC). In some suchembodiments, the ASIC may be manufactured using packaging processes thatreduce silicon substrate layers. For example, during manufacturing, anintegrated circuit may be manufactured with “active” silicon layers thatinclude functional components on top of silicon substrate layers that donot include active components. The substrate layer may be the bottommostlayer in the stack of layers, and in some cases may be the thickestlayer. Conventionally, the substrate layers are left intact followingmanufacturing, to lend structural stability to the integrated circuit.In some embodiments, the measurement circuit 214 may be manufacturedusing a process that includes removing silicon substrate layer followingmanufacture of the active layer and before packaging. The manufacturingprocess may include removing the substrate from the bottom surface ofthe wafer, which may be a side opposite from the side on which theactive components were manufactured. In some embodiments, all of thesilicon substrate may be removed. In other embodiments, substantiallyall of the silicon substrate may be removed, where “substantially”removed includes leaving only enough silicon substrate to ensure properelectrical functioning of the active layer components, without leavingsilicon substrate solely for structural support. After removal of thesilicon substrate, the integrated circuit may be encased in a packagingmaterial.

In some embodiments, placing the measurement unit 214 in close proximityto the sensors 212 and lesions 204A may limit the distance traveled bythe electrical signals thus reducing signal attenuation. The reductionin signal attenuation may be particularly significant at higherfrequencies, since electrical wires tend to exhibit low-pass frequencyresponse. By reducing the distance traveled by the signals, the cut-offfrequency of the electrical path between the signal source and thelesions may be increased, thereby increasing the range of frequenciesthat can be used in a diagnosis or a treatment. As a result, the abilityto differentiate types of tissues or cells can be significantlyenhanced. Placing the measurement unit 214 in close proximity to thesensors 212 and lesions 204A may increase the cut-off frequency up to 1MHz in some embodiments, up to 10 MHz in other embodiments, or up to 25MHz in yet other embodiments. For comparison, when measurement unit 214is located in the handle 208, the cut-off frequency may be limited toless than 500 KHz.

It should be appreciated that embodiments are not limited to thesensor(s) 212 being EIS sensors or being driven to perform EISoperations. In some embodiments, the sensor(s) 212 may be or include oneor more electrical, mechanical, optical, biological, or chemicalsensors. Specific examples of such sensors include inductance sensors,capacitance sensors, impedance sensors, EIS sensors, ElectricalImpedance Tomography (EIT) sensors, pressure sensors, flow sensors,shear stress sensors, mechanical stress sensors, deformation sensors,temperature sensors, pH sensors, chemical composition sensors (e.g. O₂ions, biomarkers, or other compositions), acceleration sensors, andmotion sensors. These sensors may include known, commercially-availablesensors.

In some embodiments, the measurement unit 214 included in the invasivedevice 210 may be configured to drive the sensors 212 and/or processresults from the sensors to generate data to be sent back along theguidewire 206 to the handle 208. This may be the case, for example, inembodiments in which treatment recommendations are to be generated bythe medical device 200. Data indicative of characteristic(s) of a lesion204A may be transmitted along the length of the guidewire 206. To limiteffects of noise during such a transmission, in some embodiments themeasurement unit 214 may include an analog-to-digital converter (ADC) orother component to generate digital data for transmission via acommunication channel (e.g., one or more wires) running through theguidewire 206.

In accordance with embodiments described herein, the clinician 202 maytreat the lesion 204A in accordance with one or more treatmentrecommendations generated by the medical device 200. While notillustrated in FIG. 2 , the medical device 200 may include a controllerto generate and output such treatment recommendations for treatment ofthe lesion 204A. The controller may, in some embodiments, be implementedas a lesion analysis facility, implemented as executable code that is tobe executed by at least one processor of the medical device 200. Thelesion analysis facility may analyze characteristic(s) of the lesion204A determined by the medical device 200 (e.g., by invasive probe 210)in connection with configured information regarding one or moretreatment recommendations. As one specific example, discussed in detailbelow, the lesion analysis facility may compare the characteristic(s) ofthe lesion 204A to conditions associated with various treatmentrecommendations and output a treatment recommendation when thecharacteristic(s) satisfy the condition(s) for that treatmentrecommendation.

In some embodiments, the processor to execute the lesion analysisfacility and the storage medium (e.g., memory) storing the lesionanalysis facility and the configured information for the treatmentrecommendations may be disposed within the handle 208. The lesionanalysis facility executing on the processor(s) in the handle 208 maytherefore receive from the measurement unit 214, via the communicationchannel of the guidewire 206, data indicative of one or morecharacteristics of the lesion 204A.

In other embodiments, however, the processor to execute the lesionanalysis facility and the storage medium (e.g., memory) storing thelesion analysis facility and the configured information for thetreatment recommendations may be disposed separate from the guidewire206 and handle 208, such as by being disposed in a separate computingdevice. The computing device may be located proximate to the guidewire206 and handle 208, such as by being located within the same room. Thecomputing device may alternatively be located remote from the guidewire206 and handle 208, such as by being located in a different room of thesame building or geographically remote from the guidewire 206 and handle208. In embodiments in which the processor/medium are separate from theguidewire 206 and handle 208, the computing device may receive the dataindicative of the one or more characteristics of the lesion 204A via oneor more wired and/or wireless communication networks, including a directwire from the handle 208 to the computing device, a Wireless PersonalArea Network (WPAN) between the handle 208 and the computing device, aWireless Local Area Network (WLAN) between the handle 208 and thecomputing device, a Wireless Wide Area Network (WWAN) between the handle208 and the computing device, and/or the Internet. Accordingly, in someembodiments the handle 208 may include one or more network adapters tocommunicate via one or more networks.

When treatment recommendations are generated by the medical device 200,the treatment recommendations may be output by the medical device 200,for presentation to the clinician 202 and/or any other user. The outputmay be via one or more networks to another device and/or to one or moredisplays, such as display 216, or other form of user interface. In theexample of FIG. 2 , the lesion analysis facility may execute on aprocessor disposed within the handle 208 and generate treatmentrecommendations, and the recommendations may be output via a wirelessnetwork adapter of the handle 208 to the display 216 for presentation tothe clinician 202. Other forms of user interface may be used, asembodiments are not limited in this respect. Any suitable visual,audible, or haptic feedback may be used. For example, if a treatmentrecommendation is to recommend between removal of a lesion using eitheran aspiration catheter or a stent-retriever, the handle 208 may includea light emitting diode (LED) or other visual element for each option,and present the treatment recommendation by illuminating the appropriateLED. As another example, if a treatment recommendation relates to amanner of operating a stent-retriever and is, in particular, arecommendation of when to begin extraction following a waiting time, asignal to begin extraction may be output using a haptic signal providedvia a vibration unit incorporated into the handle 208. Those skilled inthe art will appreciate that, as with the computing device discussedabove, elements of the user interface may be disposed within the handle208 or separate from (or even remote from) the handle 208.

Power may be provided to the invasive probe 210 via a power cableextending along a length of the guidewire 206. The power cable mayconnect to a power supply in the handle 208, which may be a battery, anenergy harvester, a connection to grid power supply, or other energysource, as embodiments are not limited in this respect.

In some embodiments, the handle 208 may include one or more sensors, notillustrated in FIG. 2 . The sensor(s) incorporated in the handle 208 maymonitor operation of the medical device 200, to inform a manner in whicha treatment was performed by the clinician 202. For example, anaccelerometer or other movement sensor may be arranged in the handle208, to detect movement of the handle 208 that governs movement of theguidewire 206 and invasive probe 210. For example, by monitoring theaccelerometer, a determination may be made of whether the clinician 202performed multiple treatments to remove a lesion (e.g., multiple passeswith an aspiration catheter or stent-retriever) or was able to extractthe lesion with only a single pass.

In some embodiments, the handle 208 may be removable from the guidewire206 and may be reusable between operations. Accordingly, while aninvasive probe 210 and/or guidewire 206 may be arranged not to bereusable and may instead be arranged to be disposable for hygienicreasons, the handle 208 may be arranged to be removably attached to theguidewire 206 and reused with other guidewires 206 and invasive probes210. For example, the guidewire 206 and the handle 208 may havecomplementary interfaces to allow the handle 208 to connect with theguidewire 206 and interface with components of the guidewire 206 (e.g.,a communication channel, a power cable) and the invasive probe 210.

The clinician 202 may operate the medical device 200 via a userinterface of the medical device, which includes a display 216 and may beat least partially disposed within the handle 208. For example, thehandle 208 may enable the clinician 202 to move the guidewire 206, andthe invasive probe 210, forward and back within the vasculature and/ortrigger operations of the invasive probe 210.

Operations of the invasive probe 210 may depend on components of theinvasive probe 210. For example, the invasive probe 210 may include thesensor(s) 212 to sense one or more characteristics of the lesion 204A.The invasive probe 210 may additionally include the measurement unit 214to operate the sensors to detect the one or more characteristics, suchas by operating the one or more sensors to apply an electrical signal tothe lesion 204A and make one or more measurements of the lesion 204Aduring and/or following application of the electrical signal. In someembodiments, the invasive probe 210 may include one or more componentsto treat a lesion 204A, including by implanting a stent and/or byremoving the lesion 204A. Lesion removal components may include thoserelated to any suitable techniques for removal of lesions, asembodiments are not limited in this respect. In some embodiments, forexample, an invasive probe 210 may include stent-retriever components(e.g., a balloon) to perform a lesion retrieval using a stent, and/oraspiration catheter components to aspirate a lesion into a catheter. Theinvasive probe 210 may additionally include other sensors not shown inFIG. 2 , including, for example, optical coherence tomography (OCT)sensors.

The user interface of the medical device, which may be incorporated inwhole or in part in the handle 208, may therefore enable the clinician202 to perform a number of different operations with the invasive probe210. For example, a user interface of the handle 208 may enable theclinician 202 to trigger sensors 212 and measurement unit 214 to applyan electrical signal and/or make a measurement of the lesion 204A,and/or to perform one or more treatment operations to treat the lesion204A.

While an example has been described in which medical device 200 mayinclude treatment components to perform one or more operations to treata lesion 204A, it should be appreciated that embodiments are not solimited. In some embodiments, medical device 200 may be a guide wire foradditional treatment devices that are inserted along the guide wire tobe positioned proximate to the lesion 204A and to treat the lesion 204A.For example, after insertion of the invasive probe 210 and guidewire206, the clinician 202 may insert another device along the length of theguidewire 206, or may remove the guidewire 206 and invasive probe 210and then insert a new device. The newly-inserted device may be, forexample, a stent implanter, an aspiration catheter, a stent-retriever,or other device to treat the lesion 204A. In some embodiments in whichan additional device is inserted, the handle 208 may be compatible withthe additional device, such that the additional device and the handle208 may have compatible interfaces and a user interface of the handle208 may be used to operate the additional device.

In addition, while an example has been provided in which a clinician 202manually operates the medical device 200 in accordance with treatmentrecommendations, embodiments are not so limited. In alternativeembodiments, the medical device 200 may treat a lesion automatically,based on input from sensors 212. For example, as should be appreciatedfrom the brief discussion above and the detailed discussion below, themedical device 200 may generate treatment recommendations on a manner inwhich to treat the lesion 204A. In some embodiments, the medical device200, in accordance with the treatment recommendations and without userintervention (though, in some embodiments, under supervision of aclinician 202) insert and/or operate an aspiration catheter,stent-retriever, stent implanter or other device to treat the lesion204A in accordance with the treatment recommendations.

It should be appreciated that embodiments are not limited to operatingwith medical devices that are invasive or include an invasive componentthat is inserted within the body of an animal. For example, non-invasiveprobes may have measurement units and/or sensors (such as EIS sensors)that operate as described herein, including operating using frequenciesor features selected as described herein or using models trained asdescribed herein. Such non-invasive devices may be, for example, usedfor diagnosis and/or treatment of skin lesions.

It should also be appreciated that techniques described herein are notlimited to use with insertable devices such as a guidewire or other toolthat may be inserted and then removed, but may also be used withimplantable devices. For example, measurement units and sensors of thetypes described herein may be used with stents, such as where thesensors are positioned directly on the stent. In this way, monitoring ofthe tissues in the region where the stent is positioned may be performedonce and after the stent is in place. The sensors may sense one or morecharacteristics (e.g., composition) of the tissues in the region wherethe stent is placed. The sensed characteristics may be used to infercharacteristics of one or more biological structures contacted by thestent, to make determinations regarding the one or more biologicalstructures. For example, the system may be used to determine whether atissue that the stent is contacting is healthy or whether scar tissue orother non-healthy tissue is forming, or whether an occlusion has formed.

FIG. 3 illustrates an example of an invasive probe 210 with which someembodiments may operate. The invasive probe 210 of the example of FIG. 3includes a mesh 300 that is arranged similarly to a stent. The invasiveprobe 210 may be operable as a stent-retriever in some embodiments. Inother embodiments, the invasive probe 210 may not be operable as astent-retriever but may include the mesh 300 or another structure toprovide multiple points of contact between sensors and a lesion so as todetect characteristics of a lesion with greater accuracy than may bepossible using only a single sensor.

Though, it should be appreciated that in some embodiments (not theembodiment of FIG. 3 ), an invasive probe 210 may include only onesensor, which may be located, for example, at a distal end of theinvasive probe 210. Such a sensor may be implemented as two electrodes,one of which may apply an electrical signal to a lesion and one of whichmay receive the applied signal. Based on a comparison of the appliedsignal to the received signal, one or more determinations may be made,as discussed in detail below.

The inventors have recognized and appreciated, however, that includingadditional sensors in the invasive probe 210 may enable more detailedinformation to be determined. For example, including additional sensorsin the invasive probe 210 may enable information on a composition of alesion to be made with more precision as compared to only a singlesensor. Such additional sensors may enable, for example, an impedancespectrum to be determined for each of multiple locations along theinvasive probe, such that, in some cases, different impedance spectramay be determined, at different locations, for the same lesion. This mayinclude, for example, determining an impedance spectrum using eachsensor. Each impedance spectrum in this case would be the impedancespectrum of a biological material, of the lesion, that a sensor (withits two electrodes) contacts. Some lesions may include multipledifferent biological materials (e.g., different tissues or cells, ordifferent plaque materials). In a case that each sensor of the invasiveprobe contacts a different biological material, each sensor maydetermine a different impedance spectrum, for each different biologicalmaterial. Though, it may be the case that, for some lesions, two or moresensors of the invasive probe may contact the same biological materialand, in such a case, may generate the same or substantially the sameimpedance spectra. Accordingly, in some embodiments, the invasive probemay operate each sensor to generate an impedance spectrum for abiological material of the lesion. Generating an impedance spectrum foreach of multiple biological materials of the lesion (i.e., multipleimpedance spectra for each lesion) contrasts with determining a singleimpedance spectrum for the lesion as a whole. Techniques for determiningcomposition of a lesion using multiple sensors, including throughperforming EIS, are discussed below.

Accordingly, FIG. 3 illustrates an example of an invasive probe 210having multiple sensors arranged along an exterior and/or interiorsurface of the probe 210. The sensors 302 (including sensors 302A, 302B,302C, 302D, generically or collectively referred to herein as sensor(s)302) may be arranged along the structure 300. In some embodiments, eachsensor may be or include one or more electrodes to apply an electricalsignal and/or detect an applied electrical signal.

In some embodiments, while not illustrated in FIG. 3 , the invasiveprobe 210 may include a balloon to, when inflated, expand the structure300 outward, to better contact a lesion. During use, for example, thestructure 300 may be wholly or partially inserted into a lesion, such asuntil sensors located at a distal end of the structure 300 detect thatthey have passed to a far side of the lesion, after which the structure300 may be expanded using the balloon until sensors 302 detect contactat multiple points. The inflation of the structure 300 may be controlledby a controller of the invasive probe 210 (e.g., measurement unit 304)or may be controlled by a lesion analysis facility disposed elsewhere inthe medical device and/or by a clinician via a user interface of amedical device.

In some embodiments, a measurement unit 304 may operate the sensors 302to perform one or more measurements, including by generating one or moreelectrical signals to apply to a lesion and analyzing data generated bysensors 302. The analysis of the data generated by the sensors 302 mayinclude performing an analog-to-digital conversion of the data to betransmitted along a guidewire to an outside of a patient, such as to alesion analysis facility or user interface as discussed above.

While examples have been provided in which the sensors 302 areelectrical sensors, it should be appreciated that embodiments are not solimited. For example, the sensors 302 may be or include one or moreelectrical, mechanical, optical, biological, or chemical sensors.Specific examples of such sensors include inductance sensors,capacitance sensors, impedance sensors, EIS sensors, ElectricalImpedance Tomography (EIT) sensors, pressure sensors, flow sensors,shear stress sensors, mechanical stress sensors, deformation sensors,temperature sensors, pH sensors, chemical composition sensors (e.g. O₂ions, biomarkers, or other compositions), acceleration sensors, andmotion sensors.

Examples of Sensors and Sensing Techniques

As discussed above, in some embodiments a measurement unit of aninvasive probe may operate sensors of the invasive probe to perform anElectrical Impedance Spectroscopy (EIS). FIGS. 4-11 describe examples ofways in which such sensors and measurement units may be arranged, anddescribe examples of techniques for operation of such sensors andmeasurement units. It should be appreciated, however, that embodimentsare not limited to operating in accordance with the examples for EISdescribed in this section.

The techniques described in this section with regard to FIGS. 4-11 allowfor a discrimination of tissues and/or biological materials of a lesionof a duct of an animal, including of a mammal such as a human.“Discrimination” should be understood here to mean the possibility,given by this method, of distinguishing between lesions of differentcompositions, for example by determining one or more types of cells(e.g., red blood cells and/or white blood cells, or different types orstates of endothelial cells) of the lesion and/or one or more types ofother biological material (e.g., plaque materials such as cholesterol)of the lesion. More generally, the discrimination made possible bytechniques described in this section includes determining at least oneitem of information relating to a tested lesion. Examples of informationthat may be determined through these techniques are given later.

The cell discrimination method 10, as illustrated schematically in FIG.4 , comprises a first step 12 of determining a frequency spectrum of theimpedance of a lesion that is tested.

Spectrum should be understood here to mean a set of pairs of values ofthe impedance of the lesion, the latter being able to be complex, and ofa corresponding frequency. This spectrum may thus be discrete andcomprise only a finite number of pairs. These pairs may notably beseparated by several Hz, even by several tens of Hz, even by severalhundreds of Hz. However, in other embodiments, the spectrum determinedin this step is continuous, pseudo-continuous or discretized, over afrequency band. Pseudo-continuous should be understood to mean that thespectrum is determined for successive frequencies separated by 100 Hz orless, preferably by 10 Hz or less, preferably even by 1 Hz or less. Thefrequency band over which the impedance of the tissue is determinedextends, for example, from 10 kHz, preferably 100 kHz. In effect, at lowfrequencies, the membrane of the tissue/material of the lesion acts asan electrical insulator, so that the impedance is very high and, aboveall, varies little. Moreover, the frequency band over which theimpedance of the tissue/material of the lesion is determined extends,for example, up to 100 MHz, preferably 1 MHz. In effect, at highfrequencies, the wall of the tissue/material that make up the lesionbecome transparent from an electrical point of view. The measuredimpedance is therefore no longer representative of the biologicalmaterial. This spectrum may be a frequency spectrum of the real partand/or of the imaginary part and/or of the modulus and/or of the phaseof the complex impedance of the lesion.

This first step 12 of determination of a frequency spectrum of theimpedance of the lesion may notably be performed as describedhereinbelow, in connection with FIG. 5 .

First of all, during a step 14, two, preferably three, even morepreferably four electrodes are placed in contact with the lesion to betested, the electrodes being linked to an alternating current generator.The measurement with four electrodes is preferred because it makes itpossible to implement two electrodes to pass the current into the lesionto be tested and to measure the potential difference between the othertwo electrodes. This makes it possible to improve the accuracy of themeasurement. Then, during a step 16, an alternating current is appliedbetween the electrodes contacting the lesion. Then, by varying thefrequency of the current applied during a step 18, the correspondingvoltage is measured, at the terminals of the electrodes for differentfrequencies. Finally, during a step 20, the ratio between the voltagemeasured and the current applied is calculated, for each of thefrequencies for which the measurement has been performed. This ratiogives the impedance of the lesion tested, as a function of themeasurement frequency. The calculated ratios make it possible to definea frequency spectrum of the impedance of the lesion.

When the spectrum is continuous or pseudo-continuous, it may berepresented as illustrated in FIG. 6 , in the form of a curve giving, inthis particular case, the modulus of the impedance of the lesion as afunction of the frequency, the latter being plotted according to alogarithm scale. It should be noted here that a logarithmic scale isused on the x axis.

In a step 22 of the discrimination method 10 of FIG. 4 , differentmodels of the impedance of the lesion, that is to say differentelectrical circuits that may model the lesion, are then chosen. Here,models are chosen that include a constant phase element, and not acapacitance. In effect, it has been found that a constant phase elementmodels more realistically the behaviour of the lesion than acapacitance.

A constant phase element (or CPE) has an impedance Z_(CPE) of the form:

$\begin{matrix}{Z_{CPE} = \frac{1}{\left( {j\omega} \right)^{\alpha}Q_{0}}} & \lbrack 1\rbrack\end{matrix}$ or: $\begin{matrix}{{Z_{CPE} = \frac{1}{\left( {j\omega Q_{0}} \right)^{\alpha}}},} & \lbrack 2\rbrack\end{matrix}$

-   -   in which:        -   j is the square root of −1 (j²=−1);        -   ω is the specific pulsing of the current (ω=2πf, in which f            is the frequency of the current);        -   Q₀ is a real parameter of the constant phase element; and        -   α is another real parameter of the constant phase element,            lying between 0 and 1, such that the phase φ_(CPE) of the            constant phase element is equal to −απ/2.

Hereinafter in the description, a constant phase element whose impedanceis given by the equation [1] above is chosen by way of example.

The models of the impedance of the lesion may notably be chosen fromthose described hereinbelow, with respect to FIGS. 7-10 . Obviously, thesimpler the model, the simpler the calculations. However, a complexmodel may better correlate to the spectrum of the impedance obtained bythe measurement and therefore give more accurate results.

According to a first model 24 illustrated in FIG. 7 , the impedance ofthe lesion is modelled by a first resistance 26 mounted in series with aparallel connection 28 of a constant phase element 30 and of a secondresistance 32.

In this case, the total resistance Z_(tot) of the lesion is of the form:

$\begin{matrix}{{Z_{tot} = {R_{1} + \frac{R_{2}}{1 + {\left( {j\omega} \right)^{\alpha}Q_{0}R_{2}}}}},} & \lbrack 3\rbrack\end{matrix}$

-   -   in which:        -   Z_(tot) is the total impedance of the first model 24            representing the lesion;        -   R1 and R2 are the resistance values of the first 26 and            second 32 resistances.

Such a model describes particularly well a lesion covering measurementelectrodes, like a set of individual parallel mountings, each individualmounting being made up of an individual resistance in series with aparallel mounting of an individual resistance and of an individualcapacitance. Such a mounting makes it possible to model a distributionof the time constant over all of the surface of the measurementelectrodes, according to different circuits in parallel whose parametersmay be different, each of these circuits in parallel representingdifferent tissue/material of a lesion. Thus, the fact that thetissues/materials of the lesion may exhibit different electricalproperties, notably a different resistance and/or capacitance, ismodelled.

A second model 34, illustrated in FIG. 8A, complements the model 24 ofFIG. 7 , by the series mounting of a second constant phase element 36.The impedance Z_(CPE,2) of this second constant phase element 36 mayalso be chosen to be of the form:

$\begin{matrix}{{Z_{{CPE},2} = \frac{1}{\left( {j\omega} \right)^{\beta}Q_{1}}},} & \lbrack 4\rbrack\end{matrix}$

-   -   in which:        -   β is a real parameter lying between 0 and 1, such that the            constant phase of this second constant phase element is            equal to −βπ/2; and        -   Q₁ is a real parameter of the constant phase element.

The total impedance Lot of the lesion according to this second model 34is therefore given by the following equation:

$\begin{matrix}{Z_{tot} = {\frac{1}{\left( {j\omega} \right)^{\beta}Q_{1}} + R_{1} + {\frac{R_{2}}{1 + {\left( {j\omega Q_{0}} \right)^{\alpha}R_{2}}}.}}} & \lbrack 5\rbrack\end{matrix}$

A variant 34′ of the second model 34 is shown in FIG. 8B, and differsfrom the model of FIG. 8A by the addition of a capacitance C in parallelwith the circuit of FIG. 8A, for a better fit of the impedance curve athigh frequencies.

A third model 38, illustrated in FIG. 9 , corresponds to the model ofFIG. 7 , mounted in parallel with a third resistance 40, of resistanceR₃. In this case, the total impedance Z_(tot) of the lesion is given bythe equation:

$\begin{matrix}{\frac{1}{Z_{tot}} = {\frac{1}{R_{3}} + {\frac{1}{R_{1} + \frac{R_{2}}{1 + {\left( {j\omega Q_{0}} \right)^{\alpha}R_{2}}}}.}}} & \lbrack 6\rbrack\end{matrix}$

Finally, a fourth exemplary model 42 is illustrated in FIG. 10 . Thismodel 42 comprises, as illustrated, a first resistance 26, mounted inparallel with a series mounting of a constant phase element 30 and of asecond resistance 32.

The total impedance Z_(tot) of the lesion is given, for this model 42,by the equation:

$\begin{matrix}{\frac{1}{Z_{tot}} = {\frac{1}{R_{1}} + \frac{R_{2}}{1 + {\left( {j\omega Q_{0}} \right)^{\alpha}R_{2}}}}} & \lbrack 7\rbrack\end{matrix}$

The discrimination method then continues with a step 44, during which,for each model chosen in step 22, the impedance of the constant phaseelement 30 which optimizes the correlation between the model of theimpedance of the lesion and the spectrum determined in step 12 isdetermined.

This step of optimization of the correlation between the model of theimpedance of the lesion and the spectrum determined in the step 12 maybe implemented by any optimization method known by those skilled in theart. By way of example, the least squares method may be implemented,which allows for a practical and relatively simple implementation ofthis step 44.

In practice, the other parameters of the different models, other thanthose of the impedance of the constant phase element, are alsodetermined during this step 44. These elements may also be useful forobtaining information on the lesion tested and/or on thetissues/materials of which it is composed.

An intermediate step 46 of the discrimination method 10 may then beprovided. This step 46 consists in determining the model which seems tobest correlate with the measured spectrum of the impedance of thelesion. This best model may for example be that which minimizes thestandard deviation with the measured spectrum. Hereinafter in thedescription, the case in which the model 24 is retained as thatcorrelating best to the measured spectrum of the impedance of the lesionis assumed.

During a step 48, an effective capacitance (or apparent capacitance) ofthe lesion is deduced from the parameters of the impedance of theconstant phase element and from the corresponding model.

Theoretically, this effective capacitance is representative of a set ofindividual capacitances of elements of the cell structure. The effectivecapacitance is representative of distributed local capacitances ofelements of the cell structure. These elements of the cell structure maynotably be all or some of the nuclei of the cells of the cellularstructure and also other parts of the cells such as the golgi apparatus,vesicles, mitochondrion, lysosome and other elements which may play arole in membrane interaction. The effective capacitance may also beinfluenced by the geometry of cells and the space between cells. Theeffective capacitance is a model which allows for a representation ofthe electrical membrane behaviour of a part or of all of a lesion. Thismodel makes it possible to relevantly discriminate the tissues/materialsof a lesion.

More practically, this effective capacitance is determined byidentifying the impedance of the lesion with a model comprisingindividual parallel mountings, each individual mounting comprising atleast one individual resistance and one individual capacitance. Eachmounting may notably comprise, preferably consist of, a first individualresistance in series with a parallel mounting of an individualcapacitance with a second individual resistance. These individualmountings aim to model the behaviour of each tissue/material of thelesion. The effective capacitance is then the capacitance resulting, inthe lesion, from the presence of all the individual capacitances.

In the case of the model 24 (or 34 or 34′), the determination of theeffective capacitance may notably be performed as follows. The impedanceof the model 24 with a constant phase element is compared with theimpedance of an equivalent or identical model, in which the constantphase element is replaced by an effective capacitance. The calculation,strictly speaking, of the effective capacitance may then be performed bycomparing the real part and/or the imaginary part and/or the phaseand/or the modulus of the impedance of the model chosen for the lesionwith a constant phase element with the identical model in which theconstant phase element is replaced by an effective capacitance.

In the case of the model 24 (or 34 or 34′), for example, by introducinga time constant

$\tau_{0} = {C_{eff}\frac{R_{1}R_{2}}{R_{1} + R_{2}}}$

into the equation of the admittance of the model 24, directly deducedfrom the equation [3], the equation [8] below is obtained:

$\begin{matrix}{{Y_{tot} = {{\frac{1}{R_{1}}\left\lbrack {1 - {\frac{R_{2}}{R_{1} + R_{2}}\left( {1 + {\frac{R_{1}R_{2}}{R_{1} + R_{2}}{Q_{0}\left( {j\omega} \right)}^{\alpha}}} \right)^{- 1}}} \right\rbrack} = {\frac{1}{R_{1}}\left\lbrack {1 - {\frac{R_{2}}{R_{1} + R_{2}}\left( {1 + \left( {j\omega\tau_{0}} \right)^{\alpha}} \right)^{- 1}}} \right\rbrack}}},} & \lbrack 8\rbrack\end{matrix}$

from which a formula for the effective capacitance may be deduced, inthe form:

$\begin{matrix}{C_{eff} = {Q_{0}^{1/\alpha} \times \left( {\frac{1}{R_{1}} + \frac{1}{R_{2}}} \right)^{{({\alpha - 1})}/\alpha}}} & \lbrack 9\rbrack\end{matrix}$

In the case where another model of impedance of the lesion with aconstant phase element is chosen, it is possible to determine acorresponding equation of the effective capacitance. To do this, it issufficient to calculate the impedances R₁, R₂, Z_(CPE) and Z_(CPE,2), ifappropriate, of the model 24 or 34 or 34′, as a function of theparameters of the chosen model, for the model 24 or 34 or 34′ to beelectrically equivalent to the model of the impedance of the lesion. Theeffective capacitance may then be calculated by replacing R₁, R₂, Z₀ andα with the corresponding values, expressed as a function of theparameters of the chosen model.

The cell discrimination method 10 then continues with a step 66 ofdeduction of an item of information on the tissues/materials of thelesion, from the effective capacitance determined previously.

This deduction may notably be made by comparing the value of theeffective capacitance determined in the step 48 with pre-establishedvalues. The pre-established values may notably be obtained during testsperformed on tissues of known compositions, in known media, and withknown test conditions. The pre-established values may be groupedtogether in a database of effective capacitance values, groupingtogether the effective capacitances measured for different types ofcells and/or different conditions of different cells and/or in differenttest conditions. The effective capacitance value may be compared to adatabase of effective capacitances of cell type and conditionsusceptible to be found in the present measurement. For the comparison,the effective capacitance Ceff may be used together with otherparameters. The comparison may not be an exact match and includes thedetermination whether the effective capacitance value falls or notwithin a pre-determined range.

It is thus possible to discriminate the tissues/materials of the lesion,that is to say to determine at least one of the following items ofinformation:

-   -   the type of tissues and/or other biological materials in the        lesion;    -   the composition of the lesion, notably if the latter is composed        of different types of biological materials or of        tissues/cells/other biological materials in different states;    -   when the lesion is composed of tissues, the types of cells        included in the tissue and/or the number of layers of cells        present in the tissue;    -   when the lesion is composed of other biological materials, such        as plaque materials, the types of materials included in the        lesion; and/or    -   the state of cells included in tissues of the lesion, notably if        the cells are in a healthy state, in an inflamed state, in a        degenerated state, notably if there are one or more cancerous        cells, in an infected state.

As an example, FIG. 18 represents, in diagram form, the effectivecapacitances 68, 70, 72, 74 determined in the context of a testconducted according to the method described previously.

In the context of a test, cells were cultivated until the confluence ofthe cells was obtained. In the case of the exemplary test which wasconducted, two days of culture were required in an incubator at 37° C.and 5% CO2, to obtain, by confluence, the tissues to be tested. Thedetermination of the spectrum of the impedance of the different tissuesto be tested was performed using an impedance spectroscopy system. Thespectrum was determined between 1 kHz and 10 MHz, by applying analternating voltage estimated to be fairly low so as not to electricallyexcite the cells being studied, but sufficient to have correctmeasurements. In the example of the test conducted, an amplitude of 20mV of the alternating voltage was retained.

The effective capacitance 68 is that of the test medium, static, alone.This test medium is a cell culture medium. The effective capacitance 70is that of bovine aortic endothelial cells (BAEC). The effectivecapacitance 72 is that of bovine aortic smooth muscle cells (BAOSMC).Finally, the effective capacitance 74 is that of blood platelets (orthrombocytes). As this diagram shows, the effective capacitances of thedifferent types of cells exhibit values clearly different from oneanother, which makes it possible to effectively distinguish between thedifferent types of cells with accuracy, without risk of confusion.

Thus, one advantage of the discrimination method described is that itallows for the discrimination of tissues/materials in a lesioncontacting the electrodes, from a simple measurement of a frequencyspectrum of an impedance of the lesion to be tested. The resultsobtained are accurate. There is no need to proceed with a normalizationof the measured impedance, nor to proceed with a reference measurementin the absence of any sample to be tested. The method may thus beimplemented without the need for prior sampling of cells or of acellular structure to be tested, and may be implemented in vivo in someembodiments.

It should be noted, in the case where an effective capacitance isdetermined, that this single value is often sufficient to discriminatethe tissues/materials of the lesion. The parameters of the chosen modelof the impedance of the lesion to be tested may also be compared topre-established values to specify the result of the comparison of theeffective capacitance. For example, when cells of a tissue are inflamed,the junction between the cells is more loose. The resistance at lowfrequency—that is to say the resistance 32 of the model 24 forexample—is then lower, compared to healthy cells. A comparison of thevalue of this resistance with a value pre-established for healthy,non-inflamed cells may then make it possible to determine the inflamedstate of these cells.

It should also be noted that the other parameters of the model may beconsidered to discriminate the tissues/materials of a lesion. However,these other parameters may also make it possible to determine additionalitems of information on the lesion tested. Thus, for example, R₂ or thesum R₁+R₂ of the resistances 26, 32 of the model 24 may be considered todetermine the thickness of a cellular structure, when a lesion includestissues. To do this, the values R₂, and possibly R₁, are determined,notably concomitantly with the determination of the impedance of theconstant phase element, so as to optimize the correlation of the model24 with the measured impedance spectrum. The value R₂ or the sum R₁+R₂may then be compared to corresponding values, predetermined in knownconditions, for example in vitro. These predetermined values may notablybe stored in a data store.

As stated previously, the method may easily be implemented in thecontext of devices that may be inserted into an animal subject, such asinserted into vasculature of a human subject.

By way of example, FIG. 11 illustrates an example 100 of a system forimplementing the method as described previously.

The system 100 essentially comprises means 102 for measuring theimpedance of a lesion 104, here a single-layer tissue of confluentcells, dipped in a medium 105, for example blood, and an electroniccontrol unit 106, linked to the measurement means 102, to implement themethod and discriminate the tissue of the lesion 104 as a function ofthe measured impedance.

The measurement means 102 here comprise an electrical generator 108 ofalternating current, linked to two electrodes 110, 112 in contact withthe lesion 104. The measurement means 102 also comprise a device 114 fordetermining the intensity passing through the lesion 104, linked to saidlesion 104 by two electrodes 116, 118 in contact with the lesion 104.The electronic control unit 106 is linked to the electrical generator108 and to the intensity measurement device 114, in order to be able todetermine the impedance of the lesion 104, for example from themeasurement of the voltage and of the intensity at the terminals of theelectrodes 110, 112, 116, 118.

The electrodes 110, 112, 116, 118 consist of an electrically conductivematerial, such as gold for example.

Here, advantageously, the measurement means 102 further comprise amedical device 120 that may be inserted in an animal subject, here aninvasive probe 120. In this case, the electrodes 110, 112, 116, 118, thealternating voltage generator and the intensity measurement device maybe fixed onto this medical device. The medical device is for example asdescribed in the application FR3026631 A1 MEDICAL DEVICE PROVIDED WITHSENSORS HAVING VARIABLE IMPEDANCE filed on 2014 Oct. 3, the entirecontents of which, and in particular the discussion of implantablemedical devices including measurement devices, are incorporated hereinby reference.

In this case, the alternating electrical generator 108 may include anarmature, such as the body of the medical device or an antennaelectrically insulated from the body of the medical device, adapted toemit an electrical current under the effect of an electromagnetic fieldemitted by an interrogation unit external to the stent 120. Theelectrodes may then form a sensor with variable impedance, the impedanceof which varies as a function of the cellular structure which coversthem. Finally, the electronic control unit may receive an item ofinformation relating to the impedance between the electrodes, notably byemission of a magnetic field by an antenna fixed onto the body of theimplantable medical device 120.

The stent 120 may thus make it possible to check the correct progress ofthe healing of the endothelium, after the stent 120 has been fitted. Ineffect, such a stent 120, in cooperation with the electronic controlunit, makes it possible to determine, by implementing the method of FIG.4 , whether the cellular structure which is formed on the surface of theendothelium essentially comprises healthy endothelial cells, inflamedendothelial cells, smooth muscle cells and/or platelets.

The invention is not limited to the examples described hereinabove andnumerous variants are possible, while within the scope of the definitiongiven by the attached claims.

Thus, for example, it is possible to choose a single model of theimpedance of the lesion in the step 22. In this case, it is notnecessary to carry out the optimization for a number of models. Themethod is therefore simpler and faster to implement in this case. It isnotably possible to proceed in this way when a model is considered asmore relevant.

Moreover, in some examples described, the discrimination of thetissues/materials is based essentially on the calculated effectivecapacitance and on its comparison with pre-established values. As avariant, however, it is possible to proceed with the discrimination ofthe tissues/materials from parameters of the chosen model of theimpedance of the lesion. However, it seems that the comparison of justthe value of the effective capacitance is both simple and allows for areliable discrimination of the tissues/materials.

FIG. 19 shows an example of a system 300 made in accordance with aspectsof the present disclosure. This system comprises a measurement module301 with may be part of an implanted device, for example a stent, or ofa device for in vitro cultivation of cells.

The measurement module comprises at least two electrodes and may be asdescribed above with reference to FIG. 11 .

The system 300 also comprises an internal processing unit 302 that isconfigured for example to generate an impedance spectrum from data fromthe measurement module.

The system 300 may comprise an emitter 303 to wirelessly transmit data(the data from the measurement module 301 and/or the impedance spectrumdetermined by the internal processing unit 302) to a receiver 304, whichmay be external to the body in case the measurements take place in vivo.The transmission may take place under any wireless protocol such asRFID, NFC, Bluetooth, WiFi, either radio or Infrared, inter alia. Insome embodiments, the transmission may include transmission via one ormore wired and/or wireless local and/or wide-area networks, includingthe Internet.

The system 300 may comprise an external processing unit 305 to computethe impedance spectrum (in the case of receiving from the emitter 303the data from the measurement module 301) and/or the various parametersand effective capacitance C_(eff) based on the received data and displaymeans 306 such as a LCD screen to display information relating to thetype and/or condition of cells determined based upon comparison of avalue representative of C_(eff) with reference data. To determine thevarious parameters and effective capacitance, the external processingunit 305 may be configured with information regarding one or moreequivalent circuit models for an impedance, and determine the parametersof at least one of the model(s), such as in the manner discussed above.The external processing unit 305 may also be configured to select one ofthe models, following determination of the parameters of the model(s),as a model from which to determine the effective capacitance, asdiscussed above. The external processing unit may make the selectionbased on a degree of fit between the equivalent circuit model and theimpedance spectrum. The system may provide, based on the at least onetype and/or condition of cells thus identified, informationrepresentative of an evolution of a healing process, for example,information regarding a current status of an area in which (e.g., tissueto which) a procedure was performed (including positioning of an implantsuch as a stent) and/or provide information regarding a change over timein the status of the area that may be reflective of a response to theprocedure in the area, such as a healing or scarring response.

The external processing unit may be a special-purpose device thatincludes specialized hardware such as an ASIC, EEPROM, or othercomponent specially configured to perform the operations of the externalprocessing unit described above. In other embodiments, the externalprocessing unit may be a general-purpose device such as a laptop ordesktop personal computer, a server, a smart/mobile phone, a personaldigital assistant, a tablet computer, or other computing deviceincluding mobile computing devices. In the case that the externalprocessing unit is implemented with a general-purpose device, thegeneral-purpose device may include one or more processors and anon-transitory computer-readable storage medium (e.g., an instructionregister, an on-chip cache, a memory, a hard drive, a removable mediumsuch as an optical medium) having encoded thereon instructions forexecution by the processor(s), where the instructions cause theprocessor to carry out the operations described above as performed bythe external processing unit. The internal processing unit may, in someembodiments, be any appropriate IC chip or other hardware component withprocessing capabilities. The external and internal processing units maybe located proximate to one another (e.g., within a same room, or within5 feet) or may be located remote (e.g., in different parts of a buildingor complex of buildings) or geographically remote (e.g., miles apart)from one another, such as in the case that the external processing unitis implemented in a server and data is transmitted via one or morenetworks or the Internet.

In a variant, as shown in FIG. 20 , part of the processing is carriedout in a distant server 310 to which data is transmitted via theinternet for example.

EXAMPLES

FIG. 25A shows a collection of amplitude and phase of an impedancespectra measured for cellular structures comprising respectively threecell types, i.e. platelets, smooth muscle cells and endothelial cells.

COMPARATIVE EXAMPLES

First, an equivalent circuit model without CPE is used, consisting of adouble layer capacitance Cdl in series with a solution resistance inseries with a R0Cmix (R0 resistance in parallel with Cmix capacitance).

Then, the Cmix parameter describing the impact of the cells layers onthe complex impedance is computed.

The result of the distribution of Cmix for two cell types is shown inFIG. 26A. Tt is possible to distinguish between the two cell types.However, if adding a third cell type the three cell types cannot bedistinguished any longer, as shown in FIG. 26B.

If one uses a more sophisticated approach and implement CPE elementsinto the equivalent circuit model, and uses for example the model 34shown in FIG. 8A, there are six parameters describing the system, i.e.R0, Rinf, Q0, β, Qdl and α.

These parameters can be computed so that the impedance of the equivalentcircuit model best fit the experimental impedance spectra curves in FIG.25A.

Then, one can display for each parameter the distribution of thisparameter for the three cell types, as shown in FIGS. 27A to 27F.

One can see that for each parameter the three cell types cannot bedistinguished clearly, and no linear combination of these parameters canprovide the cell discrimination that is looked for.

Examples According to the Invention

FIG. 28 shows the distribution of a value representative of theeffective capacitance Ceff for the three cell types, determined based onthe formula [8] above.

One can see that it is possible to clearly distinguish between all threecell types. The precision is over 90%. The differentiation between cellsis significantly improved compared to FIGS. 27A-27F.

If the equivalent circuit is the one 34′ of FIG. 8B, one obtains theCeff distribution of FIG. 29 .

If one considers R0-Rinf is large in respect to Rinf, the equation [8]can be simplified as Ceff=(1−α)/α

The resulting distribution of Ceff is shown in FIG. 30 . One can seethat the three cell types can still be distinguished with a precision ofabout 85%.

The distributions shown in FIGS. 28-30 may serve as reference data forcell type determination.

For example, an impedance spectrum may be measured in similar conditionsas the impedance spectra of FIG. 25A, and based on this spectrum thevalues of parameters R0, Rinf, Q0, β, Qdl and a are determined. Thisdetermination may be based on least square fitting of the impedancecurves of amplitude and phase with the equivalent circuit model 34 ofFIG. 8 .

Then, once the parameter values R0, Rinf, Q0 and a are known, theeffective capacitance Ceff can be computed and the value compared withthe distribution of FIG. 28 to determine to what cell type itcorresponds. For example, a low value of Ceff in nF/cm2 will indicatethat the cells are of first type; a value between about 50 and about 100that the cells are of type 3, and a value of over about 100 that thecells are of type 2.

Example Biological Structure Analysis Techniques

As discussed above, the effective capacitance of a biological structure,including cells, tissues, and/or lesions (including lesions comprisingcells and/or other materials), may be determined based on capturedimpedance measurements and employed to identify the composition of thebiological structure (e.g., the cells and/or tissue in the lesion). Theinventors have appreciated, however, that using the effectivecapacitance to identify the composition of a lesion or otherwisecharacterize a lesion may not be the most effective option under allconditions.

For example, using the effective capacitance to discriminate betweendifferent cell types in a lesion may be very effective (e.g., achieve95% accuracy) where the measurements of each cell type are capturedunder controlled conditions (e.g., same temperature conditions, sameflow conditions, etc.). The distribution of effective capacitancesdetermined for platelets, smooth muscle cells, and endothelial cellsunder controlled conditions is illustrated by the histogram in FIG. 21A.As shown, the effective capacitance of platelets, smooth muscle cells,and endothelial cells has little overlap. In particular, the effectivecapacitance of platelets is generally below approximately nanofarads persquare centimeter, the effective capacitance of smooth muscle cells isgenerally between 40 and 90 nanofarads per square centimeter, and theeffective capacitance of endothelial cells is generally above 90nanofarads per square centimeter. Because there is little overlap,effective capacitance may be used to reliably differentiate betweenthese different biological structures.

Effective capacitance, however, may be less reliable when discriminatingbetween different biological structures under less controlled oruncontrolled conditions. This may include varying temperatureconditions, varying flow conditions, or other changes. Such variationsmay be present during in vivo measurements. The distribution ofeffective capacitances determined for platelets, smooth muscle cells,and endothelial cells under uncontrolled conditions is illustrated inthe histogram in FIG. 21B. As shown, the effective capacitance ofplatelets substantially overlaps with the effective capacitance ofsmooth muscle cells. Further, the effective capacitance of the smoothmuscle cells substantially overlaps with the effective capacitance ofendothelial cells. The overlap between the effective capacitance ofdifferent cell types reduces the performance of cell discriminationtechniques that use the effective capacitance of a cell.

The inventors have developed techniques to identify characteristics(e.g., a type and/or state) of a biological structure, such as the typeand/or composition of a tissue and/or the type and/or composition ofcell(s), with higher reliability. Such techniques may leverage machinelearning. For example, machine learning may be employed to interpret andclassify EIS measurements to identify the composition of a biologicalstructure, such as a tissue, collection of cells, a lesion of an animal,or other structure or collection of biological materials. Using machinelearning techniques to identify characteristics of a biologicalstructure offers numerous advantageous relative to prior approaches. Themachine learning techniques disclosed herein may provide more accurateresults than were possible through use of effective capacitancedetermined under some uncontrolled conditions. For example, some trainedmodels developed using the machine learning techniques described hereinmay identify the composition of some biological structures with 99%accuracy. Further, using a trained model to identify characteristics ofa biological structure may be less computationally intensive than otheranalysis techniques. For example, using a trained model to identify thecomposition of biological structure may only require a device to performa series of multiplication and summation operations, involving weightingvalues or other values generated during the training of the model. Incontrast, identifying the composition of a biological structure based onthe effective capacitance of the lesion may require a device to performa computationally intensive process to derive the effective capacitancefrom the impedance measurements that includes fitting a model to theimpedance measurements.

In some embodiments, machine learning techniques may be employed toidentify the relative abundance or concentration of different types ofcells or tissues that are present in a lesion (e.g., a clot). In thisway, clots of the same type but having different relative amounts orconcentrations of a particular type of cell or material, such asdifferent relative amounts or concentration of red blood cells, may bedifferentiated from one another. Accordingly, in some embodiments, amodel may be trained to identify amounts of a particular biologicalmaterial (e.g., a particular type of cell, such as a red blood cell) ina biological structure. The amount of the material that is identifiedmay be an absolute amount, such as a certain volume or mass of thematerial, or other value indicative of an amount of the material. Inother embodiments, the amount of the material that is identified may bea relative value, such as an amount relative to amounts of one or moreother materials, including an amount relative to an overall amount ofother materials in a biological structure. For example, a ratio may bedetermined that identifies an amount of a biological material (e.g., redblood cells) in a lesion relative to a whole of the lesion. The ratiomay be a ratio by volume, by mass, or according to any other suitablevalue reflective of an amount of the type of biological material in thelesion.

In some embodiments, machine learning techniques described herein may beused to quantify the relative amounts or concentrations of the differenttypes of cells or tissues that constitute or are otherwise part of alesion or other tissue. For example, in one embodiment it may bedetermined, using machine learning techniques, that a clot includes 50%red blood cells, 30% fibrin and 20% platelets. As another example, anembodiment may determine a relative amount of material for only one typeof material, such as that a lesion is 50% composed of red blood cells,without specifically identifying the materials of the other 50% of thelesion.

The inventors have further appreciated that the direct application ofmachine learning techniques to tissue and/or cell classification mayyield unsatisfactory results, and appreciated the value of specific waysof leveraging machine learning. For example, directly training modelsusing machine learning techniques with EIS measurements alone may yielda trained model that classifies tissue or other biological structureswith an accuracy that may be undesirably low in some environments (e.g.,an accuracy below 80%). The inventors have appreciated that thisundesirably-low accuracy may arise when using only raw EIS measurementsto train models, and may stem from the amplitude and phase points in theEIS measurements being correlated while typical machine learningtechniques assume that the received features are independent (e.g., notcorrelated).

The inventors have appreciated that using derived features in additionto the raw EIS measurements to train a model using machine learningtechniques may improve the performance of the resulting model. Derivedfeatures include values that are generated from an analysis of(including computations performed on) raw ETS measurements. Such derivedfeatures may include values indicating a change in EIS measurementbetween frequencies, such as between adjacent frequencies of a set offrequency points to be collected. The derived features may additionallyor alternatively be derived from performance of one or more statisticalcomputations on raw EIS measurements. Example derived features that maybe employed include: a phase maximum frequency of the EIS measurements,an n-quantile of the EIS measurements, a first derivative of the EISmeasurements, and a second derivative of the EIS measurements.

Training a model using a set of features that includes derived featuresin addition to features present in the ETS measurements (e.g., datavalues that are included in a set of EIS measurements, and may be usedas features, as opposed to values derived from the EIS measurements) mayyield trained models that can, for example, identify a particular typeof tissue in a lesion with up to 99% accuracy.

The inventors have also recognized that use of machine learning with EISmeasurement data may be hampered by the resource constraints of somemedical devices that may produce the EIS measurements. For example, insome embodiments, an implantable and/or insertable medical device(including devices as described elsewhere herein) may need to identifyone or more characteristics of a biological structure with a limitednumber of EIS samples (e.g., 10 samples) due to various designconstraints. Such a medical device may include an invasive probe (e.g.,invasive probe 210) that can only remain in a duct of an animal for alimited amount of time without causing injury, limiting the time overwhich measurements may be made and thus limiting the number of samplesthat can be collected. Such a medical device may additionally oralternatively have a limited processing capability that is only capableof processing a limited number of EIS measurements, or have a limitedbandwidth for communicating measurements, or a limited storage formaintaining the measurements, or suffer from other resource constraints.These constraints on the amount of data that can be collected, and thatcan be provided to a trained model or be used to train a model, mayundermine the effectiveness of machine learning techniques.

Recognizing this difficulty, the inventors have appreciated the value ofa process to train a machine learning model that can accurately identifycharacteristics of a biological structure given only a limited number ofsamples (e.g., 10 EIS samples). An example of such a model trainingprocess is shown by process 2200 in FIG. 22 . The model training process2200 may use training data including multiple sets (e.g., 5 sets, 10sets, 15 sets, sets, 50 sets, 100 sets, 500 sets, 1000 sets, or morethan 1000 sets) of EIS measurements that each include a number ofsamples (e.g., 5, 10, 20, 50, 100, 500, 1000 or more than 1000measurements per set) where each sample is associated sample ofimpedance at a particular frequency of an applied electrical signal.Each of the sets of EIS measurements may be characteristic of aparticular biological sample under a particular set of conditions, andin some embodiments different training sets may correspond to differentbiological structures. The training process 2200 may seek to identifythe particular frequencies in the multiple sets of EIS measurements thatprovide the best indication of the particular characteristic of thebiological sample to be identified, and/or that provide data that bestdifferentiates different biological structures. Accordingly, thetraining process 2200 may be used to select a subset of the trainingdata corresponding to a particular set of frequencies (e.g., a set of 10frequencies), construct a model using the subset of the training data,and analyze the performance of the trained model to determine whetherthe performance is sufficient. If the trained model's performance isinsufficient, a new subset of the training data corresponding to adifferent set of frequencies may be selected and the process may berepeated. After a suitable combination of frequencies has beenidentified and corresponding trained model created, a medical device mayidentify one or more characteristics by capturing EIS measurementsassociated with the combination of frequencies (e.g., the set of 10frequencies) and applying the captured EIS measurements to the trainedmodel or interpreting the EIS measurements using coefficients (e.g.,weighting values) or rules derived from the trained model that may beused to differentiate biological structures.

As discussed above, FIG. 22 shows an example training process 2200 thatmay train a model to identify one or more characteristics of a tissueand/or cell based on a small number of samples (e.g., 10 EIS samples).The model generation process 2200 may be performed by a medical deviceand/or performed by a computer system in communication with the medicaldevice that provides the resulting trained model and/or coefficients orrules derived from the trained model to the medical device. As shown inFIG. 22 , the training process 2200 includes a block 2202 of receivingtraining data, a block 2204 of selecting a subset of the training data,a block 2206 of identifying features in the subset of the training data,a block 2208 of preparing the identified features for training, a block2210 of training a model using machine learning, and a block 2212 ofdetermine whether a performance target was reached.

In block 2202, the system may receive training data. The particularcomposition of training data may depend upon the desired characteristicsto be identified. For example, the model may be trained to differentiatebetween different types of tissues and/or cells in a lesion. In thisexample, the training data may include multiple sets (e.g., 20 sets) ofEIS samples (e.g., 100 samples per set). Each of the sets of EIS samplesmay be associated with a particular type of tissue and/or cell to beidentified (e.g., platelets, smooth muscle cells, and endothelial cells)under certain conditions. The EIS samples within each set may beindicative of an impedance magnitude and/or phase of a biologicalstructure at a particular applied frequency.

In block 2204, the system may select a subset of the training data touse to train the model during one iteration of training. The subset ofthe training data may include multiple sets of EIS measurements and moreparticularly include, for each set, a subset of EIS measurements ascompared to the originally-input sets. The subsets may include, forexample, EIS measurements for only certain frequencies of appliedsignals.

The identified subset of the training data may be subsequently employedto train the model in blocks 2206-2210. If the performance of theresulting trained model fails to meet the appropriate performancetargets, the system may return to block 2204 to determine a new subsetof the training data that is different from the previously determinedsubset of the training data. For example, a first subset of the trainingdata may include the EIS measurements from each of the sets of samplesthat correspond to the frequencies f₁, f₂, and f₃. In this example, thesystem may subsequently determine that the model trained using thissubset of the training data performed poorly. Accordingly, the systemmay return to block 2204 and select a second subset of the training datathat may include the EIS measurements from each of the sets of samplesthat correspond to the frequencies f₁, f₃, and f₅.

It should be appreciated that, at least in some circumstances, themeasurement data may be at least partially distorted by the presence ofnoise. Accordingly, to statistically mitigate the effect of noise on thedata, the biological material may be sampled numerous times using thesensors. For example, the biological material may be sampled such thatat least three spectra are obtained in less than three seconds, at leastfive spectra are obtained in less than three seconds, or at least tenspectra are obtained in less than three seconds. Using multiple spectramay increase the degree of confidence of the model.

It should also be appreciated that the system may use any of a varietyof techniques to identify the subset of the training data. In someembodiments, the system may select the subset of the training data byrandomly. For example, the system may randomly determine a set offrequencies and select the measurements from the training data thatcorrespond to the random set of frequencies. In other embodiments, thesystem may intelligently select the subset of the training data using agenetic algorithm. The genetic algorithm may, for example, take intoaccount the performance of trained models using previous subsets of thetraining data, in prior iterations, and may be applied when the systemdetermines in block 2212 that the performance target was not reached.

In block 2206, the system may identify the particular features in thesubset of the training data to use in training the model. The system mayidentify, for example, the EIS measurements in the subset of thetraining data as features. The system may also determine one or morederived features that are derived from the EIS measurements. Forexample, the system may determine a first and/or second derivative ofthe EIS measurements in the subset of the training data. The firstand/or second derivative may be calculated for each set of measurements,within the subset, and may be calculated as derivatives based on the EISmeasurements within a particular set. For example, if a measurement setincludes 10 samples, the system may calculate a first derivative foreach pair of values as a change in the amplitude or phase (or both, asdistinct values) between frequencies that are adjacent in themeasurement set. In this case, a second derivative may be calculated asa change (in amplitude or phase, depending in the nature of the firstderivative) between adjacent values of the sets of first derivatives. Inanother example, the system may determine a phase maximum frequencyand/or an n-quantile of the EIS measurements in the subset of thetraining data. N-quantiles may be the values that partition the areaunder a curve into n equal (or nearly equal) subsets. For example, thecurve defining a magnitude of the impedance over a frequency range maybe divided into n equal (or nearly equal) sections and the particularfrequencies that mark the division between the sections may be employedas a derived feature.

In block 2208, the system may prepare the identified features for use intraining the model. Preparing the data may include various functionssuch as removing noise, removing redundant information, and/or dataformatting. For example, the system may normalize the identifiedfeatures and/or identify principle components of the identified featuresusing principle component analysis (PCA).

In block 2210, the system may train a model with the identified featuresusing at least one machine learning technique. Any of a variety ofmachine learning techniques may be employed including, for example, asupport vector machines (SVM) technique, an artificial neural network(ANN) technique, a k-nearest neighbors (kNN) technique, and a decisiontree learning technique.

In block 2212, the system may determine whether the trained modelgenerated in block 2210 meets one or more performance targets. Forexample, the system may test the machine learning model using trainingand/or test data to assess the performance of the machine learningmodel. Assessing the performance may include comparing an outputgenerated for a given set of inputs to an expected output for thoseinputs, such as comparing a diagnosis of a lesion based on a set of EISmeasurements for the lesion to a known type of the lesion. Such acomparison may be performed one or more times in an iteration oftraining, based on a model generated in that iteration, to generate avalue indicative of an accuracy of the trained model. The performancetarget may include, for example, a minimum accuracy level. Tf thetrained model fails to meet or exceed the performance target, the systemmay return to block 2204 and select a new subset of the training data.As mentioned above, in some embodiments, if the performance target isnot met, in returning to block 2204 to select a new subset and determinespecific measurements to make, the system may make use of a geneticalgorithm to learn over time the frequency selections to make in block2204. For example, the system may start with multiple possiblecombinations of specific measurements and evaluate the performance ofeach combination of specific measurements. In this example, the worstperforming combinations of specific measurements may be removed fromconsideration and the best ranking combinations of specific measurementsmay be mixed together to form new combinations of specific measurements.This process of testing the performance of combinations of specificmeasurements, remove the worst performing combination of specificmeasurements, and mixing the top performing combinations ofspecification may be repeated until an appropriate combination ofspecific measurements is identified. In other embodiments, however, arandom selection process may be used in block 2204.

Otherwise, if the performance target is met or exceeded, the systemdetermines that the model has been successfully trained and the process2200 ends.

Following the process 2200, a specific set of frequencies to use for EISmeasurements that provide for adequate differentiation betweenbiological structures has been identified, and a model has been trainedto differentiate biological structures using those frequencies. Thefrequencies and the model may be stored following the process 2200. Inaddition, a medical device (including implantable and/or insertabledevices as described elsewhere herein) may be configured to measureimpedance of a biological structure at those frequencies, and a system(either the medical device itself, or in combination with anothercomputing device such as discussed above in connection with FIG. 2 ) maybe configured to identify a biological structure, identify a compositionof a biological structure, or otherwise characterize a biologicalstructure using the trained model and/or coefficients or rules derivedfrom the trained model that may be used to analyze EIS measurements toperform the identification or characterization.

Those skilled in the art will appreciate that in some techniques fortraining a model, the model learns various weighting values or othervalues (which may also be termed coefficients) to use in computationsthat produce particular outputs from inputs. In such case, a set ofthese weighting values can be said to represent the model, together withinformation on the computations to be performed on inputs using theweighting values. Accordingly, in some embodiments, as a result of theprocess of FIG. 22 (or other processes described herein that result intrained models), a set of weighting values is generated that can be usedto configure devices for subsequent use by the devices. For example, amodel trained as a result of the process 2200 of FIG. 22 may result in amodel that allows for identifying and/or categorizing lesions (or otherbiological structures of interest) based on input features determinedfrom EIS measurements for the lesion. That model may be represented as aset of weighting values that allow for identifying, from the features,one or more characteristics of the lesion. By configuring a device toperform computations using those weighting values, the device cangenerate the characteristics based on the input features, such as bygenerating a mathematical value from the EIS measurements and theweighting values and determining that the mathematical value matches aparticular characteristic or set of characteristics. Performing suchcomputations may be computationally less intensive than other forms ofanalysis that may be performed on input features to generatecharacteristics, and thus may require less time or less processingresources than alternatives.

It should be appreciated that various alterations may be made to theprocess 2200 without departing from the scope of the disclosure. In theprocess 2200 as discussed above, in block 2206 the same set of featureswere selected in each iteration based on the selected subset of trainingdata. In some cases, this set of features may be inadequate to reach theperformance target regardless of which subset of training data isselected, or there may be a set of features that may provide for moreaccurate analysis. In some embodiments, the process 2200 may optimize orotherwise improve the particular features used to train the model inaddition to optimizing or improving the particular portion of thetraining data used to train the model. For example, after the systemdetermines that the trained model failed to meet the performance targetin act 2212, or if the system determines that the trained model hasreached a performance plateau that does not meet or exceed theperformance target (or has reached a performance plateau, even if theperformance meets or exceeds the target), the system may change theparticular features employed to train the model in block 2210 inaddition to (or in place of) changing the subset of the training data.The selection of the features may be driven by a similar geneticalgorithm as was discussed above in connection with selectingmeasurement values. Thereby, the system may iterate through multipledifferent combinations of features until a combination of features thatyields desired performance is identified.

Example results obtained from using process 2200 to generate a trainedmodel that discriminates between various cell types based on 10 EISmeasurements is shown by the confusion matrices in FIGS. 23 and 24 .These confusion matrices are tables which allow direct visualization ofthe result of the model applied to specific classes of samples. Each rowof a matrix represents the instances in a predicted class while eachcolumn represents the instances in an actual class. The values of thediagonal indicate cases in which the model predicts the labeled classcorrectly. The values shown in the diagonal represent the probabilitywith which the corresponding labeled class is correctly predicted. Theoff-diagonal values represent cases in which the model confuses a classfor another, with the corresponding probability. The model was trainedto discriminate between the following five classes: 1—Bovine AorticEndothelial Cell (BAEC), 2—Bovine Aortic Smooth Muscle Cell (BAOSMC),3—Platelets; 4—Empty; 5—Intermediate. FIG. 23 shows the results from theapplication of the training data to the trained model and FIG. 24 showsthe results of the application of test data to the trained.

In some embodiments, the cell discrimination method 10 of FIG. 4 may beconfigured with particular frequencies with which to perform EISmeasurements, rather than performing a continuous or pseudo-continuousmeasurement across a spectrum. The particular frequencies may be thosethat generate impedance values that, when analysed, provide for theclearest differentiation between different biological materials. In someembodiments, the clearest differentiation may be impedance spectra withlittle overlap between the spectra, while in other embodiments theclearest differentiation may be the least overlap or similarity invalues or ranges of values that may be used to identify biologicalmaterials. For example, in some embodiments in which effectivecapacitance is determined and used to identify a biological material orotherwise determine one or more characteristics of a biologicalmaterial, the clearest differentiation may be the least overlap orsimilarity in values or ranges of values for effective capacitanceassociated with different types of biological materials.

FIG. 24A illustrates another confusion matrix, where the true labels areshown as rows and the predicted labels as columns. In this case, fourclasses were considered: “empty”, “mix”, “red” and “white.” Each classrepresents a different type of clot. For example, the class “red”represents a type of clot that is rich in red blood cells, the class“white” represents a type of clot that is rich in fibrins, the class“mix” represents a type of clot that is rich in fibrins as well as redblood cells, the class “empty” represents a case in which no clots arepresent. As shown in the matrix, which was generated based on 3000 EISmeasurements, clots of the “red” class were predicted by the trainedmodel with 100% probability, clots of the “white” class were predictedwith 94.5% probability, etc. By contrast, clots labeled “white” wereerroneously predicted as “mix” with 10.5% probability, clots labeled“mix” were erroneously predicted as “white” with 5.5% probability, etc.The EIS measurement data sets used for generating the matrix of FIG. 24Aare shown in FIG. 25B. The top chart illustrates data pointsrepresenting impedance amplitude vs. frequency for the differentclasses. The bottom chart illustrates data points representing impedancephase vs. frequency for the different classes.

While FIG. 22 was described in connection with approaches to training amodel to determine characteristics of a biological structure based oninput impedance measurements, and to determine frequencies/features aspart of learning to determine the characteristics, it should beappreciated that embodiments are not limited to using the process ofFIG. 22 to learn a relationship between impedance measurements and oneor more characteristics of a biological structure. For example, asdiscussed further below in connection with FIG. 15C, in some embodimentsa model may be trained to learn a relationship between impedancemeasurements for a biological structure and a treatment to recommend forthe biological structure (e.g., treatments for lesions, where thebiological structures are lesions). In some such embodiments, asdiscussed in connection with FIG. 15C, a system may use impedancemeasurements collected by a device as described herein to determine atreatment recommendation for a biological structure sensed by thedevice, without performing an intermediate step of identifying orcharacterizing the biological structure. In such embodiments, training amodel to learn a relationship between impedance measurements andtreatment recommendations may include identifying frequencies and/orfeatures, as discussed in accordance with FIG. 22 .

In addition, it should be appreciated that, as discussed elsewhereherein, such an embodiment that generates a treatment recommendationbased on impedance measurements may generate a treatment recommendationthat is a recommendation of a treatment option to use from among a setof treatment options (e.g., different tools to use), a recommendation ofa manner in which to perform that treatment option (e.g., a manner inwhich to operate a tool, generated before and/or during execution of thetreatment), or recommendation not to treat, among other types ofrecommendations.

Methods of Operating a Medical Device

Examples of medical devices, sensors, and manners of sensingtissues/materials of a lesion are described in detail above with respectto FIGS. 2-11 . Described below in connection with FIGS. 12-16 areexamples of techniques that may be implemented by such a medical deviceand/or that a medical device may be operated to perform.

FIG. 12 illustrates, for example, a process 1200 that may be performedby a medical device operating in accordance with some techniquesdescribed herein. The medical device of the example of FIG. 12 may be amedical device in which an invasive probe may include only a singlesensor, which may include one or two electrodes. As should be appreciatefrom the foregoing discussion, a limited amount of information regardinga lesion may be determined from a single sensor, as compared to multiplesensors arrayed along an invasive probe (e.g., in the example of FIG. 3). In the example of FIG. 12 , the sensor of the invasive probe may bedisposed in treatment devices, such as in an aspiration catheter and ina stent-retriever, and/or in a guide wire that is inserted prior toinsertion of the aspiration catheter or stent-retriever. The medicaldevice may generate treatment recommendations based on characteristic(s)of the lesion determined using the sensor.

The process 1200 begins in block 1202, in which a sensor attached to aguide wire is operated to detect one or more characteristics of a lesionthat is proximate to the sensor. Prior to the start of the process 1200,an invasive probe of the guidewire, of which the sensor is a part, maybe inserted into vasculature of an animal and moved proximate to apredicted location of the lesion. The sensor then is operated to detectwhen the sensor contacts the lesion. Contact of the lesion may bedetermined by evaluating a change over time in a value output by thesensor. For example, the sensor may output one value when contactingblood, which may be the case when the sensor is disposed in a middle ofa vessel at an area not blocked by the lesion. When the invasive probeis moved forward until contacting the lesion, a value output by thesensor may change once contact is made. In this manner, a location ofthe lesion may be determined using the single sensor. The sensor mayadditionally, in some cases, be operated to determine a length of thelesion, such as by continuing to advance the invasive probe until thesensor is no longer contacting the lesion and the output value returnsto a value that was associated with contacting blood.

In the example of FIG. 12 , using only a single sensor, the medicaldevice may not be aware of a composition of a lesion and may not be ableto make treatment recommendations regarding which treatment option maybe best to treat a particular lesion. However, the medical device may beable to produce information regarding a progress or success of atreatment, which may be used to determine whether a selected treatmentoption is being performed successfully. Based on this information, themedical device may generate a treatment recommendation on whether tochange a treatment being performed to another treatment.

In one treatment protocol that may be implemented in embodiments such asFIG. 12 , an aspiration catheter may be used as a first option fortreatment of a lesion. Accordingly, in block 1204, an aspirationcatheter is inserted into vasculature until located proximate to theinvasive probe of the guidewire and thus located proximate to thelesion. In some embodiments, a guidewire may not be inserted first, butrather the aspiration catheter may be inserted in block 1202 untilpositioned proximate to the lesion. In such a case, the sensor may be acomponent of the aspiration catheter. Embodiments are not limited inthis respect.

In block 1204, following placement of the aspiration catheter proximateto the lesion, the aspiration catheter is operated to attempt toaspirate the lesion into the catheter. Following a time, the sensor ofthe guidewire and/or aspiration catheter may be operated to determinewhether the aspiration catheter is having an effect on the lesion. Somelesions, such as hard lesions, may not be able to be aspirated using anaspiration catheter. For these lesions, other interventions (such as astent-retriever) may be used. Accordingly, in block 1204, in addition tooperating the aspiration catheter to attempt to aspirate, the sensor maybe operated to determine whether a change has been seen in the lesion.This may be done, for example, by positioning the sensor within thelesion prior to a start of aspiration, such as at a portion of thelesion closest to the aspiration catheter, and determining after a timewhether the value output by the sensor indicates that the sensor is nolonger in contact with the lesion (and is rather, for example, incontact with blood).

If during (and potentially as a result of) operation of the aspirationcatheter the sensor no longer contacts the lesion, a determination maybe made in block 1206 that the lesion is aspirating. In this case, atreatment recommendation may be generated and output indicating that theaspiration catheter appears to be successfully treating the lesion andthat continued operation of the aspiration catheter is recommended. Inthe example of FIG. 12 , the process 1200 then ends. It should beappreciated, however, that in some embodiments successive determinationsmay be made over time for whether the aspiration catheter is continuingto successfully treat a lesion, such that a change may be recommended ifappropriate or that a determination may be made of when a lesion hasbeen fully aspirated.

If, however, the value output by the sensor is not changing during theaspiration and indicates that the aspiration is not having an effect onthe lesion, a treatment recommendation may be generated and output thatan aspiration catheter is no longer recommended and that instead,another treatment option is recommended. In the example of FIG. 12 , asecond option for treatment of a lesion may be a stent-retriever.Accordingly, in block 1208, a recommendation to use a stent-retrievermay be output. In block 1210, the stent-retriever may be operated totreat the lesion by removing it with the stent-retriever. For example,the stent-retriever may be inserted until located proximate to thelesion. In some embodiments, as discussed above, the sensor with which adetection is made may be a component of a guide wire, separate from atreatment device. In such a case, the stent-retriever may be insertedalong the guide wire (or inserted along a micro-catheter inserted alongthe guidewire, following removal of the guidewire), following removal ofthe aspiration catheter, until the stent-retriever is positionedproximate to the lesion. As another example, the sensor may beintegrated with the stent-retriever and may detect when thestent-retriever is located proximate to the lesion. The medical device,through a value produced using the sensor, may generate a treatmentrecommendation regarding positioning of the stent-retriever for removalof the lesion. For example, the sensor may be used, as discussed above,to detect when an invasive probe has traversed a lesion and a distal endof the invasive probe is located on a far side of the lesion. It may bebest to position a stent of a stent-retriever across a lesion, such thatone end of the stent protrudes beyond the lesion, to aid in ensuringthat a lesion is fully captured with a stent. Accordingly, by operatinga sensor to detect a far side of a lesion, and recommending that astent-retriever be inserted until the stent or sensor extends throughthe lesion, a treatment recommendation may be made regarding properpositioning of a stent.

Once the stent-retriever is operated to remove the lesion in block 1210,the process 1200 ends.

FIG. 13 illustrates an example of a manner of operating a medical deviceto generate treatment recommendations for a lesion in accordance withanother embodiment. In the embodiment of FIG. 13 , an invasive probe mayinclude multiple sensors arrayed along an exterior of a probe, such asin the example of FIG. 3 discussed above. As should be appreciated fromthe foregoing, with such an array of sensors, several differentcharacteristics of a lesion may be determined, including composition ofthe lesion. The composition of the lesion may indicate differentbiological materials present in the lesion, such as different tissues orcells, or other biological materials such as plaque materials. In somesuch embodiments, for example, each sensor (e.g., the two electrodes ofeach sensor) may contact biological materials of the lesion, with somesensors contacting different biological materials of the lesion than doother sensors. Each sensor may then be operated, in accordance withtechniques described herein, to determine an impedance spectrum of thebiological material contacted by the sensor. This set of impedancespectra may then be used to determine a composition of the lesion, suchas by identifying different biological materials present in the lesion.This composition information may be similar to the information that maybe determined from performing a histology on the lesion. From thedifferent impedance spectra for the lesion, and/or an identification ofthe different biological materials present in the lesion (e.g., thedifferent tissues or plaque materials), characteristics of the lesion asa whole may be determined, such as by identifying (e.g., diagnosing) atype of the lesion.

For example, by performing an EIS process on the different biologicalmaterials of the lesion, it may be determined whether any of thefollowing cells or tissues are present in the lesion: platelets,fibrins, thrombi, red blood cells, white blood cells, smooth musclecells, elastic fibers, external elastic membrane, internal elasticmember, loose connective tissues, endothelial cells, or any other tissueof a tunica intima, media or externa. In addition, by performing an EISprocess on the lesion, the relative amount of each of the present cellsor tissues may be determined. As a simple example, it may be determinedthat a lesion is composed by 50% red blood cells, 30% fibrin and 20%platelets. From this information, the lesion may be categorized as oneparticular type of lesion from a set of lesions, such as by diagnosingthe lesion as being of one type of lesion rather than other types oflesions.

The process 1300 of FIG. 13 begins in block 1302, in which an invasiveprobe of a medical device is inserted into vasculature of an animalsubject and operated to detect one of more characteristics of a lesion,including a composition of a lesion. Based on the characteristics,including the composition, the medical device may in block 1304 select atreatment option to recommend. The medical device may select thetreatment option in any suitable manner, including according to atechnique described below in connection with FIGS. 14-15B.

The treatment option that is selected may be selected based on acomposition of the lesion. For example, if a composition of the lesionindicates that it is composed of smooth muscle tissue rather than athrombus, the medical device may determine that implantation of a stentis a treatment that should be recommended. This may be because thelesion is not composed of cells/materials that may be extracted, but isinstead a growth within the vessel. As another example, if thecomposition of the lesion indicates that it is a soft lesion, such as asoft lesion made of freshly-formed thrombus, the medical device mayrecommend an aspiration catheter. This may be because soft lesions arecapable of being aspirated. As a further example, if the composition ofthe lesion indicates that it is a hard lesion, such as a hard bloodclot, the medical device may recommend a stent-retriever, because it isunlikely that a hard lesion would be successfully aspirated.

Once a treatment is recommended in block 1304, the medical device may inblock 1306 monitor performance of a treatment option that is selected.The medical device may monitor the treatment using one or more sensors,such as the one or more sensors with which the characteristics weredetermined in block 1302 or one or more sensors of a treatment devicethat is operated to perform the treatment. For example, in someembodiments, following the recommendation of block 1304, a clinician mayinsert another device into vasculature of the subject (e.g., anaspiration catheter, stent-retriever, etc., as appropriate) and theother device may include an invasive probe have an arrangement ofsensors as described herein. In such an embodiment, the medical devicemay monitor the performance of the treatment using the sensors of theinvasive probe of the other device.

The monitoring of the treatment in block 1306 may produce informationregarding a status and/or progress of a treatment. For example, if thetreatment is being performed with an aspiration catheter, the monitoringmay produce information on an extent to which a lesion has beenaspirated, and/or a remaining amount of the lesion to be aspirated. Theprogress may be monitored, for example, by the medical deviceperiodically or occasionally inflating a structure (e.g., the stent-likemesh of FIG. 3 ) to contact a remaining portion of the lesion withsensors, to determine an extent of the lesion that remains. After thedetermination is made, the structure may be removed to continueaspiration of the lesion. If, on the other hand, the treatment is beingperformed with a stent-retriever, the monitoring may produce informationon an extent to which a stent has coalesced with a lesion duringinflation of the stent. For example, by monitoring sensors along anexterior of the stent (e.g., with an arrangement of sensors on a stentlike the example of FIG. 3 ), a determination may be made of whethereach portion of a stent corresponding to each sensor is fully expandedinto a lesion. This determination may be made in any suitable manner,including by monitoring a change over time in values produced by eachsensor and determining when a value for each sensor stops changing. Wheneach sensor stops changing value, this may indicate that there has beenno further change in an interaction between a lesion and a stent and, assuch, the stent is fully expanded into the lesion and the lesion iscoalesced around the stent.

Making such determinations may aid in performance of a treatment of alesion. Accordingly, in block 1308, information on a status of atreatment is output by the medical device via a user interface, forpresentation to a clinician. In addition, in block 1310, the medicaldevice may generate one or more treatment recommendations on a manner inwhich to perform the treatment. For example, when the medical devicedetermines that a lesion is fully coalesced with a stent duringoperation of a stent-retriever, as discussed above, the medical devicemay output a treatment recommendation that extraction of the stentbegin.

Once the treatment is successfully performed, the process 1300 ends.

While an example of monitoring a treatment is given in the context ofgenerating treatment recommendations, it should be appreciated thatsimilar techniques may be used to raise error messages or other messagesto a clinician regarding a status of a treatment. For example, if asensor on a treatment device indicated presence of the lesion for atime, after which the sensor no longer detects the lesion, the medicaldevice may determine that the treatment device is improperly positionedor that the lesion was lost. This may indicate either that the deviceneeds to be repositioned or, potentially more problematically, that thelesion has become an embolism. A message to the clinician via the userinterface may indicate such a potential problem.

Additionally, while the example of FIG. 13 described a manner ofoperating a medical device to provide treatment recommendations bothrelating to an initial selection of a treatment and related to asubsequent manner of performing that treatment, it should be appreciatedfrom the foregoing that embodiments are not so limited. For example, insome embodiments, a medical device may include one or more sensors asdescribed herein and may be operated to produce treatmentrecommendations on a manner of operation of that device, withoutgenerating an initial recommendation to use that device. For example, astent-retriever or aspiration catheter, as discussed above, may includeone or more sensors to generate data on a status or performance of atreatment and may produce treatment recommendations. As another example,a guidewire for treatment of a Chronic Total Occlusion (CTO) maygenerate information on a tissue/material contacted by a sensor andgenerate treatment recommendations. In a CTO procedure, the guidewiremay be inserted through smooth muscle tissue or the plaque of a bloodvessel when a solidified thrombus cannot be penetrated. Treatmentrecommendations may be made, based on sensed characteristics oftissue/material contacted by a sensor, of when a guidewire is positionedagainst the smooth muscle tissue and can be advanced and when theguidewire has been advanced through the endothelial tissue and is onceagain within the blood vessel, on a far side of the lesion. In addition,in some embodiments, one or more measurements may be taken of athickness of smooth muscle tissue or other characteristics of the vesselwall that may be informative of a risk that the guidewire will puncturethe tissue rather than navigate through the tissue. For example, if ameasurement indicates a thinning of the smooth muscle tissue on one sideof an invasive probe of the guidewire, this may indicate that theinvasive probe is at risk of puncturing the vessel wall. A treatmentrecommendation may be made to proceed more slowly and/or to withdraw theguidewire, or another recommendation may be generated.

Those skilled in the art will appreciate from the discussion herein thatthere are a variety of ways in which a medical device may be configuredto generate treatment recommendations based on characteristics of alesion and/or a status of a treatment. FIGS. 14-15B illustrate oneexample of a technique that may be used for generating treatmentrecommendations.

FIG. 14 illustrates a process 1400 that may be implemented by a medicaldevice in some embodiments for generating treatment recommendations.

The process 1400 begins in block 1402, in which the medical devicereceives one or more characteristics of a lesion. The medical device mayreceive the characteristic(s) from a component of the medical device,such as in a case that the characteristic(s) are determined using one ormore sensors included in an invasive probe of the medical device and/orby another component (e.g., a lesion analysis facility) that generatescharacteristic(s) based on data produced by the sensors. Thecharacteristic(s) may include a composition of the lesion, in someembodiments. The characteristic(s) may additionally or alternativelyinclude a location of the lesion within the body, one or more dimensionsof the lesion (e.g., a length, a thickness, etc.), a temperature of thelesion, or other information that may be determined based on the typesof sensors described above.

In block 1404, the medical device compares the characteristic(s)received in block 1402 to one or more conditions for one or moretreatment options. The medical device may be configured with informationon multiple different available treatment options, each of which may beassociated with one or more conditions that relate to one or morecharacteristics of lesions. For example, the medical device may beconfigured with one or more conditions for treatment of a lesion byimplantation of a stent, one or more different conditions for use of anaspiration catheter, and one or more further different conditions foruse of a stent-retriever. Examples of such conditions related to acomposition of a lesion are described above in connection with FIG. 13 .

The medical device may compare the characteristic(s) of the lesion tothe conditions to determine which conditions are met. In someembodiments, the sets of conditions for treatment options may bemutually exclusive, such that a lesion may meet only one set ofconditions and thus only one treatment option may be selected. In otherembodiments, the set of conditions may not be mutually exclusive, andthe medical device may determine which treatment option to recommend byidentifying the one for which the most corresponding conditions are metor the one for which the corresponding conditions are met most closely(e.g., in the case that a condition is associated with a range ofvalues, the condition for which a value most closely matches the rangeby, for example, falling in a middle of the range).

In block 1406, based on the comparison, the medical device may output arecommendation of a treatment option via a user interface of the medicaldevice, and the process 1400 ends.

While the process 1400 is described in connection with generating aninitial treatment recommendation for a treatment of a lesion based oncharacteristics of a lesion, those skilled in the art will understandhow to extend the technique to generation of treatment recommendationsduring performance of a treatment, as described above in connection withblock 1310. For example, in some embodiments, based on comparison ofcharacteristics of a lesion (e.g., composition of the lesion) to one ormore conditions for certain parameters of a treatment, such as a speedat which to extract a stent of a stent-retriever, the medical device mayoutput recommendations on such parameters.

Those skilled in the art will appreciate that there are a number of waysin which to set the conditions for treatment options that may be used inconnection with a process like process 1400 of FIG. 14 . For example,values for characteristics of a lesion to use as conditions may behard-coded into a medical device following at least some experimentationto determine a correspondence between the values, types of lesions, andsuccessful treatment with various treatment options. The inventors haverecognized and appreciated, however, the advantages of a system to learnsuch relationships and conditions based on characteristics of lesionsand information on successful treatments of lesions, among otherinformation. For example, a machine learning process, such as one thatmay include feature extraction and/or classification, may be implementedin some embodiments.

FIGS. 15A-15B illustrate an example of a machine learning process thatmay be performed in some embodiments. FIG. 15A illustrates a processthat may be implemented by a medical device, whereas FIG. 15Billustrates a process that may be implemented by a computing device(e.g., a server) in communication with multiple different medicaldevices.

The process 1500 of FIG. 15A begins in block 1502, in which a medicaldevice generates information on characteristics of a lesion. In blocks1504 and 1506, the medical device may make recommendations on treatmentoptions based on a comparison of lesion characteristics to conditionsfor treatment options as well as monitor a progress of a treatment andgenerate status information throughout the treatment. These operationsof blocks 1502-1506 may be implemented similar to the manner describedabove in connection with FIGS. 13-14 and thus, for the sake of brevity,will not be described further. In addition, in block 1506, the medicaldevice may generate information on an outcome of a treatment. Theoutcome of the treatment may indicate whether a lesion was successfullytreated, whether the lesion was dislodged and released into thesubject's body, whether multiple treatments were necessary, or otherinformation indicating an outcome. The information indicating theoutcome may be generated using sensors of the medical device, as shouldbe appreciated from the foregoing. For example, using data generated byan accelerometer in a handle of the medical device, the medical devicemay determine whether it was operated multiple times to remove a lesion.As another example, as discussed above, if a sensor was detecting alesion then stopped detecting the lesion, this may be an indication thatthe lesion has moved in the subject, including that the lesion wasdislodged and became an embolism.

In block 1508, the information generated in blocks 1502-1506 istransmitted from the medical device, via one or more wired and/orwireless communication connections and/or networks, including theInternet, to a computing device. The computing device may be, in someembodiments, geographically remote from the medical device. In block1508, following the transmission in block 1506, the medical devicereceives from the computing device (such as via the network(s) via whichthe information was transmitted in block 1508) one or more updatedconditions for treatment options. The updated conditions may identifynew values for evaluation of conditions with respect to characteristicsof lesions. The medical device may configure itself to apply the one ormore updated conditions for generation of treatment recommendations,such as through considering the one or more updated conditions in thecontext of a process like the one discussed above in connection withFIG. 14 . Once the medical device is configured with the updatedconditions, the process 1500 ends.

FIG. 15B illustrates a process that may be implemented by a computingdevice to perform a learning process on reports on treatments oflesions, to generate conditions for use in selecting treatmentrecommendations such as via a process like the one discussed above inconnection with FIG. 14 . Specifically, in the example of FIG. 15B, acomputing device analyzes reports on treatments of lesions, inconnection with information regarding characteristics those lesions, toidentify relationships between successful (and/or unsuccessful)treatments and characteristics of lesions. Through identifying suchrelationships, conclusions may be drawn about which treatment optionsare best for particular types of lesions and, based on thoseconclusions, a treatment recommendation may be generated for treatmentof a particular lesion based on characteristics of that lesion, as inthe example of FIG. 14 . Similarly, as discussed above, based oninformation regarding status or performance of a treatment,recommendations on a manner of performing a treatment (e.g., a time ator speed with which to extract a stent during a stent-retrieval) may bedetermined. While the example of FIG. 15B will be described in contextof generating conditions for an initial selection of a treatment optionto use for a lesion based on characteristics of a lesion, those skilledin the art will understand from the description below how to extend thetechnique for use with generating recommendations on a manner in whichto perform a treatment.

The inventors have recognized and appreciated that the generation ofsuch conditions and the identification of relationships betweensuccessful/unsuccessful treatments and characteristics of lesions may beadvantageously determined using a machine learning process. Variousmachine learning algorithms are known in the art and may be adapted foruse in this context. Some machine learning algorithms may operate basedon feature extraction and classification techniques, in which groups(classifications) for units are identified and an analysis of propertiesof units is carried out to determine which properties, and/or values ofthose properties, most closely correspond to or predict correctmembership in the groups. Based on these identified properties,subsequently-received unclassified units having such properties can be“classified” into one of the groups/classifications based on acomparison of the properties and/or values of the properties of theunclassified unit to the properties/values for each group. In somemachine learning applications, the groups/classifications may beidentified manually during a configuration of the machine learningprocess. In addition, or in others, the groups/classifications may bedetermined or adjusted over time by the machine learning process, suchas through creation of new groups/classifications when the machinelearning process perceives through its analysis that a new grouping maybetter characterize some units. A full discussion of machine learning isoutside the scope of this document and not necessary for anunderstanding of techniques described herein. Those skilled in the artwill understand how to implement a machine learning technique for usewith information and goals described herein.

Here, groups may be defined as treatment options or treatment outcomes,and the example of FIG. 15B will be described in this context. In thiscase, the groups may be defined by characteristics of lesions and/orstatuses of treatment. In this case, when characteristics of a lesionand/or of a status of treatment match characteristics for group, thecorresponding treatment option may be selected for output. Additionallyor alternatively, in some embodiments groups may be associated withdifferent types of lesions (each type having one or more characteristicsor ranges of characteristics different from the other types) and/orstatus of treatment, and these different groups may then be associatedwith particular treatment options or ways in which to operate atreatment device. In this latter case, when characteristics for aparticular lesion or status of a treatment match a group, thecorresponding treatment recommendation(s) for the group may be selectedfor output.

The process 1520 of FIG. 15B begins in block 1522, in which a learningfacility executing on one or more computing devices receives, over time,multiple reports on treatment of lesions by medical devices. The medicaldevices may be medical devices operating in accordance with embodimentsdescribed above. The reports may include information on a lesion thatwas treated, such as one or more characteristics of the lesion. Thereport may also include information on a manner in which a lesion wastreated, such as on one or more treatment devices that were operated totreat the lesion and the manner in which those lesions were treated.Information on an outcome of the treatment may also be included in areport, such as whether a treatment was successful, whether multipletreatments were necessary, whether a lesion was dislodged and became anembolism, or other outcomes.

The reports may contain information determined by one or more sensors ofa medical device, including examples of sensors and types of informationdescribed above. As discussed above, various types of sensors may beincluded in embodiments, including one or more electrical, mechanical,optical, biological, or chemical sensors. Specific examples of suchsensors include inductance sensors, capacitance sensors, impedancesensors, EIS sensors, Electrical Impedance Tomography (EIT) sensors,pressure sensors, flow sensors, shear stress sensors, mechanical stresssensors, deformation sensors, temperature sensors, pH sensors, chemicalcomposition sensors (e.g. 02 ions, biomarkers, or other compositions),acceleration sensors, and motion sensors. It should be appreciated thatvarious types of characteristics or other information may be generatedfrom these sensors. Any of this information may be included in reportsand used in the process 1520 for generating conditions associated withtreatment recommendations. For example, as discussed above, anaccelerometer disposed within a handle of a medical device may trackmovements of the medical device and be used to determine whethermultiple treatments were performed to treat a clot. As another example,a force sensor may indicate a force with which a stent-retriever isextracted or a set of impedance sensors may determine, based on whethera detected impedance at one or more sensors of a stent of astent-retriever varies over time during an extraction, whether a lesionis partially or fully separating from the stent during retrieval. Thoseskilled in the art will appreciate from the discussion above differenttypes of data that may be generated by sensors of a medical device forinclusion in such reports.

Reports may also include information that may be entered by a clinicianor retrieved from another system with which the medical device mayinteroperate. For example, the report may include information on aposition of a lesion within anatomy of the subject, such as whether thelesion is in a cranial artery, femoral artery, pulmonary vein, commonbile duct, or other duct. This information may be entered by theclinician via a user interface or, for example, retrieved from anothersystem such as an angiogram device.

Optionally, the reports may include information about the patients, suchas age, medical history and demographic.

The reports that are received in block 1522 may be received over timefrom a plurality of medical devices, which may be geographicallydistributed. By receiving these reports, and the contents of thesereports, over time a set of conditions and treatment recommendation thatdefine recommended or best practices may be generated.

Accordingly, in block 1524 the learning facility analyzes theinformation in the reports to identify relationships between lesioncharacteristics (and/or manners of operating treatment devices), optionsfor treating lesions having those characteristics, and successfultreatments. Based on this analysis, the learning facility may learnrelationships between these pieces of information. Such relationshipsmay indicate when certain treatment options are successful or notsuccessful, or for which types of lesions different treatment optionsare successful or not successful. In at least some of the embodiments inwhich information about the patients are obtained, the learning facilitymay learn relationship between lesion characteristics, options fortreating lesions having those characteristics, and successful treatmentsbased on the patients' information. The model may be trained to learnwhich particular piece of information, among all the informationobtained about patients, is likely to affect the probability of successof a treatment. For example, the trained model may identify that aparticular treatment is likely to have different probabilities ofsuccess depending on the age of the patient, even if all thecharacteristics of the lesion are equal. As such, different treatmentrecommendations may be provided for two patients having identicallesions but different age. As another example, the trained model maylearn that some treatments, when applied to subjects who have suffered acertain condition in the past, are less likely to succeed relative tosubjects who have not suffered such a condition, even if the type oflesions are identical.

Based on this analysis in block 1524, the learning facility (through thefeature extraction and classification processes of a machine learningprocess) may in block 1526 generate conditions for each of the treatmentoptions. The conditions may be associated with characteristics oflesions, so as to indicate different characteristics or ranges ofcharacteristics for lesions that may be successfully treated with eachtreatment option. For example, conditions may relate to a range ofvalues for a visco-elastic property of a lesion, such that avisco-elasticity in one range may be associated with treatment using anaspiration catheter and visco-elasticity in another range may beassociated with treatment using a stent-retriever. In this manner, whena lesion having a specific visco-elasticity is detected, a comparison tothese conditions may be used (as in the process of FIG. 14 ) todetermine which treatment option to recommend for that particularlesion.

In block 1528, once the conditions are generated in block 1526, theconditions may be distributed to medical devices such that the devicesmay be configured to use those conditions to generate treatmentrecommendations, as discussed above in connection with FIG. 15A. Oncethe conditions are distributed, the process 1520 ends.

While the process 1520 is discussed in FIG. 15B as a discrete process,it should be appreciated that in some embodiments the reception ofreports and determination of conditions may be a process that isrepeated over time, including continuously or at discrete intervals.Accordingly, in some embodiments the process 1520 may be performedmultiple times or, following distribution of conditions in block 1528,the learning facility may return to block 1522 to receive additionalreports and continue the learning process.

In some alternative embodiments, generation of treatment recommendationsmay be performed, using machine learning techniques, directly based onmeasurement data obtained from one or more sensors of the typesdescribed above. Being directly based on measurement data, thegeneration of treatment recommendation may be performed without havingto first characterize or identify the lesion or the type of lesion.Compared to other approaches in which measurement data are used toidentify the nature (such as the type or composition) of the lesion, andsubsequently identify a suitable treatment recommendation, this approachmakes it possible to skip an intermediate step of characterizing thelesion. This may reduce the time and/or computational resources neededto generate a treatment recommendation.

FIG. 15C illustrates a process that may be implemented by a computingdevice to perform a learning process on reports on treatments of lesionsto generate conditions for use in selecting treatment recommendations.The conditions may relate to EIS measurements, and/or featuresdetermined from EIS measurements (e.g., features present in themeasurements and/or derived features). The process of FIG. 15C may beused to train a model to identify one or more relationships between theEIS measurements or features and treatments for lesions to which themeasurements/features relate. Training the model in this manner, withrelationships(s) between measurements/features and treatments, may allowfor generation of recommendations on a treatment for a lesion withoutneeding to diagnose or identify the lesion.

The process 1540 of FIG. 15C begins in block 1542, in which a learningfacility executing on one or more computing devices receives, over time,reports including measurement data obtained using the techniquesdescribed herein. Examples of measurement data include, but are notlimited to, EIS measurements. Each set of data may include any suitablenumber of EIS samples, each of which may represent impedance informationobtained at a specific frequency. As such, each set of measurements maybe interpreted as the spectral response of a particular type of lesion.In certain cases, measurement data obtained via sensors other thanimpedance sensors may be used in process 1540. Examples of such sensorsinclude inductance sensors, capacitance sensors, Electrical ImpedanceTomography (EIT) sensors, pressure sensors, flow sensors, shear stresssensors, mechanical stress sensors, deformation sensors, temperaturesensors, pH sensors, chemical composition sensors (e.g. O₂ ions,biomarkers, or other compositions), acceleration sensors, and motionsensors.

The measurements that are received in the report may includemeasurements taken when the sensor(s) initially contacted the lesion, orotherwise during a phase of a diagnosis or treatment when measurementsare being collected for the lesion. Such measurements could be used, inaccordance with techniques otherwise described herein, to identify ordiagnose the lesion. The measurements may additionally or alternativelyinclude measurements collected during a treatment, such as during anextraction of a lesion using a stent-retriever or during anotherprocedure.

The report may also include information on a manner in which a lesionwas treated, such as on one or more treatment devices that were operatedto treat the lesion and the manner in which those lesions were treated.Information on an outcome of the treatment may also be included in areport, such as whether a treatment was successful, whether multipletreatments were necessary, whether a lesion was dislodged and became anembolism, or other outcomes.

The reports that are received in block 1542 may be received over timefrom a plurality of medical devices, which may be geographicallydistributed. By receiving these reports, and the contents of thesereports, over time a set of conditions and treatment recommendation thatdefine recommended or best practices may be generated.

Accordingly, in block 1544 the learning facility analyzes theinformation in the reports to identify relationships between measurementdata, options for treating lesions exhibiting the characteristicsrepresented in the measurement data, and successful treatments. Based onthis analysis, the learning facility may learn relationships betweenthese pieces of information. Such relationships may indicate whencertain treatment options are successful or not successful, or for whichtypes of lesions different treatment options are successful or notsuccessful.

Based on this analysis in block 1544, the learning facility (through thefeature extraction and classification processes of a machine learningprocess) may in block 1546 generate conditions for each of the treatmentoptions. Blocks 1546 and 1548 may operate substantially in the samemanner as blocks 1526 and 1528 of FIG. 15B.

It should be appreciated that, as discussed elsewhere herein, anembodiment that generates a treatment recommendation based on impedancemeasurements, in accordance with FIG. 15C, may generate a treatmentrecommendation that is a recommendation of a treatment option to usefrom among a set of treatment options (e.g., different tools to use), arecommendation of a manner in which to perform that treatment option(e.g., a manner in which to operate a tool, generated before and/orduring execution of the treatment), or recommendation not to treat,among other types of recommendations.

Examples are provided above of devices and processes for providingfeedback to a clinician during a diagnosis and/or treatment of a lesion,including providing treatment recommendations during the diagnosisand/or treatment. In some embodiments, in addition to or as analternative to providing such feedback during the diagnosis and/ortreatment, a medical device may be configured to present information ona diagnosis and/or treatment to a clinician following the operation ofthe medical device in the diagnosis/treatment. FIG. 16 illustrates anexample of such as process.

The process 1600 begins in block 1602, 1604, in which a medical deviceis operated to generate information on characteristics of a lesion andon performance of a treatment, and recommendations on a manner in whichto perform the treatment. The operations of blocks 1602, 1604 may besimilar to examples of generation of data discussed above.

In block 1606, following the treatment, the information generated inblocks 1602, 1604 is used by a chronicle generation facility to generatea chronicle of the treatment. The chronicle of the treatment may includeinformation regarding how devices were operated over time, whatcharacteristics of the lesion were detected, what recommendations weremade by the medical device, and whether those recommendations werefollowed by the clinician. If an error was detected in the treatment,such as a loss of a part or entirety of a lesion that resulted in, forexample, creation of an embolism or necessity of a subsequent treatment,the chronicle generation facility may analyze the error to determine acause of the error. For example, if sensors detected at a time that apart of a lesion separated from a stent-retriever, and at animmediately-preceding time another sensor noted application of a suddenforce to the stent-retriever, the chronicle generation facility may notethis in the chronicle. If a force applied to a stent-retriever exceededa maximum force recommendation from the medical device, or the medicaldevice was operated in any other manner inconsistent with the treatmentrecommendation, this may be noted in the chronicle. When suchinformation is included in the chronicle, recommendations may be made tothe clinician on how to avoid the error in future procedures.

In addition, in some embodiments, the chronicle generation facility mayinclude in the chronicle detailed information on a lesion and potentialcauses of the lesion, to aid a clinician in diagnosing the lesion. Forexample, while in some embodiments during a treatment a briefcharacterization of a lesion may be output (e.g., lesion is viscous), ina chronicle more detailed information on a composition may be output(e.g., lesion primarily composed of cholesterol). In addition, thechronicle generation facility may analyze the composition in context ofa location of the lesion in the subject to determine whether the lesionwas, for example, a result of an injury, a thrombus that developed atthe site of the lesion, or an embolism that became stuck at the site ofthe lesion. For example, if the lesion is primarily composed of tissuelike smooth muscle cell or atheroma, the lesion may have been a growthat the site following an injury. As another example, if the compositionof the lesion indicated it formed in an area of the anatomy having ahigh shear stress, but the lesion is located at an area of the anatomyhaving a low shear stress, this may indicate the lesion was an embolismthat became stuck at the site.

Once the chronicle has been generated in block 1606, the chronicle isoutput for presentation to a user (e.g., via a display, or stored to amemory or transmitted via a network), and the process 1600 ends.

The inventors have further appreciated that an accuracy of techniquesdescribed herein, such as an accuracy in diagnosis and/or a degree ofconfidence with which treatments are recommended for intervening onparticular types of lesions, would be increased with greater certaintythat the data used in training a model, and the data collected for alesion and used in diagnosing the lesion or in determining a manner inwhich to treat a lesion, corresponds to the lesion and not to othertissues or biological structures.

The inventors have further recognized and appreciated that, in manycases, an insertable or implantable device may, during collection ofmeasurements, be contacting more structures than just a lesion or otherbiological structure of interest. Instead, it may often be the case thata probe including one or more sensors may fully or partially contactother biological structures located adjacent to or proximate thebiological structure of interest. For example, if an insertable deviceis navigated through an animal's vasculature until it is contacting alesion of a blood vessel, and is then operated to collect measurementsfor that lesion, it is possible and perhaps likely that the sensors ofthe probe may be contacting a vessel wall in addition to contacting thelesion. As a specific example, rather than penetrating the lesion orotherwise only contacting the lesion, in some cases the insertabledevice may be located between the lesion and the vessel wall, such thatsome sensors are contacting the lesion and others are contacting thevessel wall.

In cases in which measurements are collected for one or more otherbiological structures, in addition to the biological structure ofinterest, such measurements may impact the accuracy of techniquesdescribed herein for identifying or characterizing a biologicalstructure, or determining an appropriate treatment for the biologicalstructure.

The inventors have therefore developed approaches for filtering EISmeasurements collected according to the methods described herein, toremove measurements not associated with a lesion or other biologicalstructure of interest. More particularly, the inventors have appreciatedthat filtering the collected data to remove, or least reduce the numberof, measurements that are not associated with the lesions maysignificantly increase the model's ability to accurately characterizelesions and/or provide suitable recommendations for treating lesions.The filtered data (without or with reduced measurements corresponding toother structures) may also be used to train a model in any of themanners described above.

The inventors have additionally recognized and appreciated that thereare a range of approaches to filtering of data to remove extraneous oroutlier values, and have recognized and appreciated the advantagesoffered by a technique that leverages machine learning to perform thefiltering. In such a machine learning process, a model may be trainedwith EIS measurements for a biological structure for which the modelwill “pass” measurements, as well as EIS measurements for one or moreother biological structures that may be located in an area of ananimal's body in which the biological structure of interest is locatedand for which the model will “filter” measurements. The one or moreother biological structures may be identified based on expected orpredicted anatomy of various animals' bodies in an area to be probed,such as an area in which a lesion is expected to be found or probed. Forexample, once an area of an animal's body is identified, and abiological structure that is to be measured and/or treated isidentified, during a configuration phase one or more other biologicalstructures that may be found in that area of the animal's body, and thatmay be adjacent to or proximate to the biological structure of interest,are identified. Measurements may then be collected for the biologicalstructure of interest (e.g., a particular type of lesion, such as arange of lesions of the type with a range ofcharacteristics/compositions) and/or for one or more other biologicalstructures in vitro. A model may then be trained based on themeasurements to distinguish, for that area of the animal's body, betweenthe biological structure of interest and one or more other biologicalstructures.

FIG. 17A is a flowchart illustrating a process for training a model tocharacterize lesions and/or for providing treatment recommendations withan improved degree of confidence or accuracy, through filteringmeasurements that are for biological structures other than the lesion.Process 1700 begins at block 1702, in which training data are received.The training data may include measurements obtained by samplingbiological materials using sensors of the types described above. Thetraining data may be obtained using in vitro or in vivo techniques. Thetraining data may include measurements associated with the lesions to betreated as well as measurements associated with other biologicalstructures.

The identity of the structure to which the measurements correspond mayalso be input in the training, to aid the model in learning todistinguish between measurements that correspond to the biologicalstructure of interest and other measurements.

The training of block 1702 may be carried out to train a model todistinguish between EIS measurements that are for a biological structureof interest (e.g., a particular type of lesion) and EIS measurementsthat are not for that biological structure. Such a model may thereforesort EIS measurements into one of two categories: “for” the biologicalstructure of interest and “not for” the biological structure ofinterest. In other embodiments, rather than only training a model todistinguish between those two categories, a model may be trained withEIS measurements for each of multiple different biological structuresand be trained to categorize input EIS measurements into one of thosecategories, to identify a biological structure to which each EISmeasurement most likely corresponds.

The model may be trained in block 1702 to identify, for each particularEIS measurement, whether the EIS measurement corresponds to a biologicalstructure of interest and/or a structure to which the EIS measurementcorresponds. Accordingly, the model of block 1702 may not be trained tocarry out an identification or filtering on a set of EIS measurements,but rather may process a set of EIS measurements to filter outindividual EIS measurements within the set. Such a set of EISmeasurements may include measurements from operation of an insertable orimplantable device at a particular time, such as a collection of EISmeasurements at a time at which multiple sensors of an implantable orinsertable device are used. Distinguishing between measurements in thisway may allow for distinguishing between EIS measurements collected, ata particular time or over a particular time interval, by a sensor incontact with a lesion or other biological structure of interest, and EISmeasurements collected by another sensor of the device that is notcontacting the lesion/structure but instead is contacting anotherbiological structure.

Once a model is trained to carry out filtering in this manner, thefilter may be used on training data as part of a process for training amodel to identify and/or categorize a biological structure (e.g.,lesion) based on EIS measurements. More particularly, at block 1704,training data is filtered to remove, or at least to reduce, the datathat are not associated with a biological structure of interest, such asa particular type of lesion. At block 1708, the filtered training datamay be used to train a model to recognize relationships between thefiltered data and lesion characteristics (e.g., in accordance withembodiments described above in connection with FIGS. 15A-15B), or todirectly recognize relationships between the filtered data and treatmentrecommendations (e.g., in accordance with some embodiments describedabove in connection with FIG. 15C). Block 1708 may be implemented usingany of the processes described above.

It should be appreciated that in the embodiments in which the filter ofblock 1704 is trained to learn which data subsets correspond to lesionsand which data do not, process 1700 may include a multi-step trainingmodel, the first training step being performed at block 1704, the secondtraining step being performed at block 1706. In some embodiments, thefilter of block 1704 and the model of block 1706 are trainedconcurrently, that is, as a single multi-variable problem and using thesame data. In other embodiments, however, the filter of block 1704 andthe model of block 1706 are trained using separate data and/or atdifferent times.

In some embodiments, the training of FIG. 17A may be performed inaccordance with techniques described above in connection with FIG. 22 .As should be appreciated from the foregoing, the process of FIG. 22includes an iterative approach to identifying frequencies at which tocollect EIS measurements and features to extract from EIS measurementsfor use in training one or more models and/or analyzing data inconnection with such a model to distinguish between tissues. In eachiteration of the iterative approach, different frequencies and/ordifferent features may be selected for use in training a model todetermine whether a model trained with that input may satisfy one ormore performance targets. In some embodiments, such an iterative processmay also be used for training a filter model for filtering out EISmeasurements that do not correspond to a biological structure ofinterest and for training a model to identify and/or characterize abiological structure of interest. In some such embodiments, EISmeasurements may be collected in vitro and/or in vivo for a biologicalstructure of interest (e.g., a particular type of lesion) and/or forother biological structures that may be found in an area of an animal'sbody adjacent to or proximate to the biological structure of interest.The EIS measurements may be for a wide range of frequencies. Duringtraining processes in accordance with FIGS. 22 and 17A, an iterativeprocess is used in which during each iteration, a subset of frequenciesare selected and EIS measurements for those frequencies are used intraining the two models (a filter model and a model toidentify/characterize a biological structure). Additionally oralternatively, as discussed in connection with FIG. 22 , in eachiteration a set of features may be selected, which may include featurespresent in EIS measurements and/or features derived from EISmeasurements. The iterative process may continue with selection, in eachiteration, of frequencies and/or features and training based on theselected frequencies and/or features, until a process for identifyingand/or characterizing a biological structure satisfies one or moreperformance targets. Such a performance target may be, as discussed inconnection with FIG. 22 , reaching or exceeding a desired accuracy inidentifying or characterizing biological structures.

Following such a training, an insertable and/or implantable device maybe configured to collect EIS measurements at the identified frequencies,and may be configured to apply the trained models to filter EISmeasurements to remove measurements not corresponding to a biologicalstructure of interest and to identify/characterize a biologicalstructure using the filtered EIS measurements. As discussed above, thoseskilled in the art will appreciate that applying the trained model mayinclude performing computations using weighting values or other valuesgenerated during the training. Accordingly, the insertable and/orimplantable device may also be configured with the values determined inthe training, such that the device may apply the trained model.

In some embodiments, different filters may be generated for differentparts of an animal's body (e.g., a mammal's body, including a human ornon-human body). For example, one filter may be generated to distinguishbetween heart's tissues (such as a coronary artery's wall) and a heart'slesion (such as a clot in the coronary artery), and a separate filtermay be generated to distinguish between brain's tissues (such as aninner wall of a cerebral vein) and a lesion that may be found in thebrain. Different models may also be trained for different biologicalstructures of interest, such as different types of lesions. Thedifferent models may thus include a model that is trained for aparticular biological structure of interest and for a particular area ofan animal's body in which the biological structure of interest will belocated and measured. The inventors have appreciated that havingdifferent filters for different parts of the body and/or for differentbiological structures of interest, rather than a single filter for theentire body may substantial limit the amount of data with which eachfilter is trained, thus reducing the computation involved in thelearning process. Different models may also improve the accuracy of thefilter. It should be noted, however, that not all embodiments need toutilize multiple filters for different parts of the body, as a singlefilter for a biological structure of interest may be used in somecircumstances for multiple body parts or an entire body.

FIG. 17B illustrates a process that may be used for generating a filterfor use in block 1704 of FIG. 17A. Process 1720 begins at block 1722, inwhich a body part is identified. Examples of body parts include, but arenot limited to, a heart or a specific portion thereof, a brain or aspecific portion thereof, a liver or a specific portion thereof, akidney or a specific portion thereof, a limb's vein, etc. Once a bodypart has been identified, one or more biological materials (includingtissues and/or lesions) of the body part may be identified at block1724. For example, if the heart is identified at block 1722, specifictissues of the coronary artery (e.g., smooth muscle cells, elasticfibers, external elastic membrane, internal elastic member, looseconnective tissues, and/or endothelial cells) and/or clots that maygenerally be found inside a coronary artery may be identified at block1724.

At block 1726, data associated to the biological materials identified atblock 1724 may be collected. The data may represent measurementsobtaining by sampling the biological materials, which in someembodiments is performed using sensors mounted on invasive probes. Insome embodiments, the data may include spectral measurements, such ascollections of EIS samples obtained at different frequencies, associatedto a particular biological material of the body part.

At block 1728, the collected data is labeled to refer to the specifictissue or lesion to which the data corresponds. For example, datacorresponding to measurements obtained from the endothelial cells of acoronary artery may be labeled “coronary artery endothelial cells;” datacorresponding to measurements obtained from the elastic fibers of acoronary artery may be labeled “coronary artery elastic fibers;” datacorresponding to measurements obtained from a thrombus found in acoronary artery may be labeled “thrombus in coronary artery;” datacorresponding to measurements obtained from a fibrin found in a coronaryartery may be labeled “fibrin in coronary artery,” etc.

At block 1730, a filter may be trained using the labeled data.Specifically, the filter may be trained to differentiate between dataassociated with lesions (e.g., thrombi, fibrins or other types of clots)and tissues (e.g., endothelial cells, elastic fibers, loose connectivetissues, etc.).

While the techniques of FIGS. 17A-17B were described above as relatingto filtering of measurements for other biological structures, it shouldbe appreciated that the filtering techniques are not so limited. Inother embodiments, the filtering techniques described in connection withFIGS. 17A-17B may be used to additionally or alternatively identify andfilter out erroneous measurements. Erroneous measurements may resultfrom any potential source of error, and may include EIS measurementswith magnitude and/or phase values that are incorrect. Erroneousmeasurements may impact accuracy in the same or a similar manner asmeasurements corresponding to other biological structures. Accordingly,a training process may be carried out in which known erroneousmeasurements are input together with an indication that the measurementsare erroneous, and along with measurements that are not erroneous, totrain a model to distinguish between erroneous and non-erroneousmeasurements. The trained model (which may additionally be trained todistinguish between measurements for a biological structure of interestand measurements for other biological structures) may then be used by adevice to filter measurements.

The inventors have appreciated that conventional techniques foridentifying the location of an insertable device relative to a lesion inan animal's body are often unsatisfactory. One conventional techniquefor locating clots inside vasculature, for example, involves the use ofx-ray images in connection with x-ray reflective probes. Specifically,when an invasive probe having a portion that is reflective to x-rays isinserted in a duct of an animal, the position of the probe can bemonitored using x-ray imaging. Unfortunately, clots are typically notreflective to x-rays; therefore, the x-ray image provides no indicationas to the location of the clot. As a result, the process of contactingthe clot with the invasive probe with is often onerous, and itinevitably involves numerous attempts. This process may undesirablyincrease the duration of an operation, or even worse, may lead to damageto the duct's inner walls as the clinician repeatedly steers the probein search of the clot.

The inventors have therefor recognized the advantages of a technique fordetermining whether a probe is in contact with a lesion. Accordingly,some embodiments are directed to techniques for identifying whether abiological structure being probed is a biological structure of interestlike a lesion (e.g., a clot) or another biological structure like atissue on which the lesion is formed (e.g., the inner wall of a duct).In some embodiments, machine learning techniques may be used to identifywhether a biological structure is or is not a biological structure ofinterest. Accordingly, a model may be trained using known dataidentifying measurements associated with different biologicalstructures. Once trained, the model may be able to differentiate betweenbiological structures. In some embodiments, the process of FIG. 17B isused to train the model, though alternative processes are also possible.

FIG. 17C illustrates a process that may be used to aid a clinician inguiding an invasive probe. Process 1740 begins at block 1742, in whichdata is received, for example at a processor of a medical device, fromone or more sensors mounted on an invasive probe that is inserted intoan animal (e.g., in the animal's vasculature). The received data mayinclude, at least in some embodiments, measurements of impedanceassociated with one or more biological structures of the animal.

In some embodiments, the data received in block 1742 may be multiplesets of sensor readings, collected over an interval of time. During thatinterval of time, the insertable or implantable device may, in somecases, not be moved, such that all sensors of the device are contactingthe same materials throughout those sets of measurements. The multiplemeasurements in such cases may assist in generating a high-confidenceresult in identifying or characterizing a biological structure contactedby the sensors of the device. In some embodiments, for example, atrained model for identifying or characterizing a biological structuremay produce a value indicative of a confidence of the model that sensorreadings correspond to a particular biological structure or particularcharacteristics (e.g., composition) of the biological structure. Themodel may accept additional data as input, which may adjust theconfidence higher or lower. Collecting multiple data over an interval oftime may therefore aid in producing a high-confidence result that abiological structure is or is not a biological structure of interest. Insome such embodiments, for example, the biological structure(s)contacted by a device may be sampled such the at least three spectra areobtained in less than three seconds, at least five spectra are obtainedin less than three seconds, or at least ten spectra are obtained in lessthan three seconds.

At block 1744, it may be determined whether the measurements correspondto a biological structure of interest, such as a particular type oflesion. In some embodiments, this determination may be performed in partusing a machine learning filter trained according to process 1720 (FIG.17B), which may be trained to distinguish between biological structures.

If it is determined that EIS measurements correspond to anotherbiological structure that is not the structure of interest, then inblock 1746 information may be output to a user, informing the user thatthe probe is not yet contacting the biological structure of interest.The information may be output in any suitable way, for example using adisplay or an acoustic device. Based on this information, the clinicianmay decide to continue to guide the invasive probe inside the animal,and process 1740 may repeat through blocks 1742, 1744 and 1746.

In some embodiments, the determination steps of blocks 1744 may relateto whether all EIS measurements collected by a device at a particulartime or over a particular time interval correspond to a biologicalstructure of interest. For example, the determination may be whether thedevice is fully contacting the structure of interest (e.g., lesion) orwhether one or more of the sensors is contacting another biologicalmaterial. If not all of the sensors are determined to be contacting thestructure of interest, then in block 1746 information may be outputindicating that the device is not fully contacting the biologicalstructure of interest (e.g., the lesion). In other embodiments, thedetermination step of block 1744 may be performed to identify which (ifany) sensors of the device are producing readings indicating that thesensors are contacting the biological structure of interest.Distinguishing between sensors in this manner may assist a clinician inguiding a device to properly and fully contact a biological structure ofinterest. For example, if sensors are arranged longitudinally along thelength of an insertable device, then as the device approaches and thencontacts a biological structure of interest, the sensors may over timeproduce values indicative of not contacting the desired structure, thenthe sensors at a distal end may produce values indicating that they arecontacting the structure, then all sensors may produce values indicatingthat they are contacting the structure once the device is properlysited. If the clinician moves the device too far, the distal sensors mayproduce values indicating that they are not contacting the desiredstructure.

If it is determined that the probed biological material is a biologicalstructure of interest, then in block 1750 information to that effect maybe output. Subsequently, the clinician may perform any of the followingsteps: operate the invasive probe to determine one or morecharacteristics of the lesion (as described in connection with block 104of FIG. 1 ); operate a medical device to generate and output treatmentrecommendation based on the lesion characteristics (as described inconnection with block 106 of FIG. 1 ); select a treatment option basedon the treatment recommendation (as described in connection with block108 of FIG. 1 ); and/or treat the lesion using the selected treatmentoption (as described in connection with block 110 of FIG. 1 ).

EXAMPLES

Described below are various examples of scenarios in which medicaldevices and techniques may be used. It should be appreciated, however,that embodiments are not limited to operating in accordance with any oneof these examples.

Example 1

One example of a way in which the techniques described herein may beused is with an invasive, smart guide wire. The invasive guide wire maybe used in navigating the vascular system. Using sensors and analysistechniques described herein, the invasive guide wire may characterizetissue/materials with which it is in contact and communicatecharacteristics of this tissue/material to a clinician. The invasiveguide wire may also help additional devices reach an intervention sitewithin a patient.

In this example, the guide wire comprises a sensor (preferably an EISsensor), an impedance spectrometer, and a handle. The guide wire mayalso include additional components that can be inserted along its lengthduring use. The sensor may be used to sense and characterize propertiesof the tissue/material with which it is in contact. For example, thesensor may be used to determine tissue/material composition when usedwith the impedance spectrometer to perform high frequency impedancemeasurements. Both the sensor and the impedance spectrometer arepreferentially located at an invasive tip of the guide wire so thattissue adjacent to the tip can be characterized without the need forlong electric wires connecting sensor to the impedance spectrometer.This design may reduce electronic noise that may be otherwise insertedinto electrical signals if the impedance spectrometer were locatedoutside the subject.

The handle may contain additional components, such as those forcommunicating with the user, recording and transmitting data both duringand after surgery, processing data, and powering the device. Examples ofsuch components include a feedback unit such as a display or indicatorlight readable by a user, a unit for transmitting data either wirelesslyor through a cable, a database, a processor, and a battery. The handlecan be removable from the other device components; it can also beremovably connected to circuitry on the guide wire itself.

Example 2

The guide wire described in Example 1 may be used by a clinician todetermine an optimal treatment strategy for a patient experiencing ablocked artery. The clinician can use the guide wire to characterize thetissue/material that is blocking the artery and then choose betweendifferent possible treatments based on this information. In someembodiments, the guide wire may provide treatment recommendations to theclinician based upon one or more characterizations that it has performedand, optionally, based upon data from prior treatments performed withthe aid of a guide wire.

In this example, the clinician can use the guide wire to assess andtreat an arterial lesion. The clinician can begin by steering the guidewire to the site of the thrombus, optionally using the handle, and thenpenetrating the thrombus. Next, the clinician may use the guide wire toperform a measurement of the composition of the thrombus and/or of thetissue/material that is blocking the artery. The clinician can thendetermine an optimal treatment for the blocked artery based upon theresults of this measurement. For example, the clinician may decide touse a stenting device if the blocking tissue is composed of cells fromthe arterial wall of the patient. If the blocking tissue is a thrombus,the clinician can instead decide to measure its viscoelastic propertiesand then determine whether to use an aspiration catheter or a stent toremove the clot on the basis of this information.

In some embodiments, the clinician may also receive a treatmentrecommendation from the guide wire. The treatment recommendation can bebased upon the characterizations performed by the guide wire on thearterial lesion and/or based upon data collected during previous uses ofa guide wire.

Upon conclusion of treatment, the clinician may remove the handle fromthe guide wire and insert the appropriate interventional device with theaid of the guide wire.

Example 3

An additional example of a device which may be used in accordance withthe techniques described herein is a smart stent-retriever. Thestent-retriever may be used to retrieve blood clots from a patient.Using sensors and analysis techniques described herein, the invasivestent-retriever may characterize a clot with which it is in contact andcommunicate characteristics of this tissue/material to a clinician.

In this example, the stent-retriever comprises at least one sensor(preferably at least one EIS sensor and/or EIT sensor), a measurementunit, and a handle. The stent-retriever may comprise multiple sensors atmultiple strategic locations so that information regarding a blood clotwith which it is in contact can be obtained from multiple locationswithin the clot. When a stent-retriever includes more than one sensor,the sensors may be able to sense different properties of the clot withwhich it is in contact. For example, the stent-retriever may compriseone or more sensors capable of sensing the integration of the clot withthe stent-retriever, one or more sensors capable of sensing the positionof the stent-retriever as a function of time, and/or one or more sensorscapable of sensing the force applied to the clot. The integration of thestent-retriever with the clot may be determined by sensing theinductance and/or EIT signal of a stent as a function of time. Becausethe inductance and EIT values of the stent will vary with the expansionof the stent and the surrounding environment, constant values of theseproperties indicate that the stent has reached its maximal expansion andintegration into the clot. A motion sensor may be used to sense theposition of the stent-retriever as a function of time. This feature canenable the clinician to understand the movement of the stent-retrieverwithin the patient and to determine the number of passes thestent-retriever has made during retrieval of a clot. Stress sensors mayalso be included to measure the force applied by the stent-retriever toa clot or tissue/material.

The measurement unit of the stent-retriever may be an impedancespectrometer and/or a tomography unit. This unit is preferentiallylocated close to the tip of the stent-retriever so that a clot adjacentto the stent-retriever can be characterized without the need for longelectric wires connecting sensor to the measurement unit. This designmay reduce electronic noise that may be otherwise inserted intoelectrical signals if the impedance spectrometer were located outsidethe subject.

The handle may contain additional components as described in Example 1.It can also comprise a robotized pulling mechanism to allow accurate andautomatized retrieval of the clot.

Example 4

The guide wire described in Example 1 and the stent-retriever describedin Example 3 may be used together by a clinician to determine andexecute an optimal treatment strategy for a patient experiencing ablocked artery. The clinician can use the guide wire to characterize thetissue/material that is blocking the artery and then use thestent-retriever to retrieve the clot and/or thrombus. Optionally, datacan be collected during clot retrieval and uploaded to a database forlater analysis.

In this example, a clinician can use a combination of smart devices totreat a patient experiencing a blocked artery. The clinician may beginby inserting the guide wire with a sheath and using the guide wire (withan invasive probe, as discussed above) to assess the lesion as describedin Example 2. If the clinician decides to next use a stent-retrieverbased upon information and/or a recommendation provided by the guidewire, the clinician will remove the guide wire, leaving the sheath inplace, and insert the stent-retriever along the sheath and steer it intothe clot and/or thrombus. Once the stent penetrates the clot and/orthrombus, the sensors incorporated into the stent-retriever can senseaspects of the clot and/or thrombus and provide this information to theclinician as a function of time (e.g., on an external display). Forexample, the EIS and/or EIT sensors can characterize the integration ofthe stent with the clot and/or thrombus and the shape and composition ofthe clot and/or thrombus. The stent-retriever may also use data fromprior clot and/or thrombus retrievals to provide treatmentrecommendations to the clinician. Treatment recommendations can include,for example, signals that integration of the stent-retriever with theclot and/or thrombus is optimal and/or recommendations regarding theappropriate speed and force with which to pull the clot and/or thrombus.

At this point, the clinician may act upon the information and/orrecommendations provided by the stent-retrieve to retrieve the clotand/or thrombus. The clinician may decide to use an automatic pullingmechanism incorporated in the stent-retriever to retrieve the clot. Theautomatic pulling mechanism may then pull the clot and/or thrombus at aspeed and using a force determined by the stent-retriever based upondata received from a database of prior clot and/or thrombus retrievals.If the clot and/or thrombus detaches form the stent retriever, thestent-retriever will signal the clinician using an alarm. The clinicianmay then penetrate the clot and/or thrombus once again and restart theretrieval process.

At the conclusion of the clot and/or thrombus retrieval, all the datacollected during the intervention can be transferred to a database forlater analysis.

Example 5

Another example of a device which may be used in accordance with thetechniques described herein is a smart aspiration-catheter. Theaspiration-catheter may be used to retrieve blood clots from a patient.Using sensors and analysis techniques described herein, the invasiveaspiration-catheter may characterize a clot with which it is in contactand communicate characteristics of this tissue/material to a clinician.

In this example, the aspiration-catheter comprises at least one sensor(preferably at least one EIS sensor and/or EIT sensor), a measurementunit, and a handle. As in Example 3, the aspiration-catheter maycomprise multiple sensors at multiple strategic locations so thatinformation regarding a blood clot with which it is in contact can beobtained from multiple locations within the clot. When anaspiration-catheter includes more than one sensor, the sensors may beable to sense different properties of the clot with which it is incontact. For example, the aspiration-catheter may comprise one or moreof the sensors described in Example 3 (i.e. one or more sensors capableof sensing the integration of the clot with the aspiration-catheter, oneor more sensors capable of sensing the position of theaspiration-catheter as a function of time, and/or one or more sensorscapable of sensing the force applied to the clot). Theaspiration-catheter may also comprise an additional sensor capable ofmonitoring blood flow within the aspiration-catheter.

The measurement and the handle unit of the aspiration-catheter areidentical to the measurement unit and handle of the stent-retrieverdescribed in Example 3.

Example 6

The guide wire described in Example 1 and the aspiration-catheterdescribed in Example 5 may be used together by a clinician to determineand execute an optimal treatment strategy for a patient experiencing ablocked artery. The clinician can use the guide wire to characterize thetissue/material that is blocking the artery and then use theaspiration-catheter to retrieve the clot and/or thrombus. Optionally,data can be collected during clot retrieval and uploaded to a databasefor later analysis.

In this example, a clinician can use a combination of smart devices totreat a patient experiencing a blocked artery. The clinician may beginby inserting the guide wire and using it to assess the lesion asdescribed in Example 2. If the clinician decides to next use anaspiration-catheter based upon information and/or a recommendationprovided by the guide wire, the clinician will then insert theaspiration-catheter along the guide wire, steer it into the clot and/orthrombus, and begin the aspiration process. During aspiration of theclot and/or thrombus, an external display will provide information tothe clinician regarding removal progress, the shape and composition ofthe clot and/or thrombus as sensed by the EIS and/or EIT sensors, andthe passage of the clot and/or thrombus through the aspiration-catheter.The smart aspiration-catheter may determine also determine the optimaltime to begin removal of the clot and/or thrombus based upon integrationof the aspiration-catheter with the clot and signal this condition tothe clinician. The clinician may then begin to remove the clot and/orthrombus. If the clot and/or thrombus detaches from theaspiration-catheter, the aspiration-catheter may signal the clinicianusing an alarm. The clinician may then penetrate the clot and/orthrombus once again and restart the retrieval process. When the sensorsdetect that the thrombus has been fully aspirated and passed along thetube of the aspirator, another message indicating successful removal maybe generated and output.

At the conclusion of the clot and/or thrombus retrieval, all the datacollected during the intervention can be transferred to a database forlater analysis.

Example 7

The guide wire described in Example 1 may be used to treat a patientexperiencing Chronic Total Occlusion (CTO). In this case, the patient'sartery is blocked by an old and rigid thrombus that may be difficult forthe clinician to penetrate in order to reestablish blood flow. Theclinician may use the smart-guide wire to sense the position of thelesion and pass through the lesion. During operation, the guide wire canprovide information to the clinician regarding when penetration of thelesion is initiated and when passage through the lesion to the lumen ofthe artery occurs. If the thrombus is too rigid to penetrate, theclinician can instead pass the guide wire through the arterial walladjacent to the lesion. In this case, the guide wire can providecontinuous information to the clinician regarding its position withinthe atheroma/plaque. This may help the clinician to avoid puncturing thevessel.

Example 8

The guide wire described in Example 1 may be used by a clinician indiagnosis and/or treatment of peripheral pathologies. Examples ofperipheral pathologies include thrombi formed in deep veins or arteries,or thrombi formed in artificial veins or arteries. The guide wire may beused to determine an optimal treatment strategy for a patientexperiencing a peripheral pathologies. The clinician can use the guidewire to characterize the tissue/material that is blocking the duct andthen choose between different possible treatments based on thisinformation. In some embodiments, the guide wire may provide treatmentrecommendations to the clinician based upon one or morecharacterizations that it has performed and, optionally, based upon datafrom prior treatments performed with the aid of a guide wire.

Example 9

As an additional example, any one of the foregoing invasive probes maybe used to estimate the age of a clot (e.g., a thrombus). The age of theclot (i.e., the life of the clot since its formation) may be determinedon the basis of one or more characteristics of the clot, such as thecomposition of the clot. Different treatments or combination oftreatments may be provided based on the age of the clot, as determinedfrom these characteristics. For example, one treatment may berecommended if the clot is less than fourteen days and a differenttreatment may be recommended if the clot is more than fourteen days.

Additionally, or alternatively, at least some of the devices andtechniques described herein may be used to identify whether a biologicalstructure is a healthy tissue. For example, the devices/techniques maybe used to determine whether a wall of a vessel is healthy or whether anatheromatous plaque or a calcification has formed on the vessel wall. Insuch a case, a biological structure that is contacted by one of thedevices described herein may be a vessel wall or an atheromatous plaque(or other lesion), and the techniques described herein may be used todetermine whether it is one of those biological structures. Based on theidentification, different treatment recommendations may be provided.

Methods of Operating a Medical Device for Use in Oncology

The inventors have recognized and appreciated that conventionaltechniques for examination of potentially cancerous cells are oftenunsatisfactory. For example, one conventional technique for examiningpotentially cancerous cells uses a needle to remove a tissue sample. Toaid a clinician in guiding the insertion of the needle, conventionalimaging systems such as x-ray, ultrasound, or magnetic resonant imaging(MRI) are used. However, images generated using these techniques areoften inaccurate or blurred, thus making it difficult for the clinicianto determine whether the needle is in contact with the cell or tissuebeing targeted. As a result, diagnosis and/or treatment of cancerouscells using such techniques is often inaccurate. As a result, whentrying to determine whether a particular lesion is cancerous, asignificant risk is that a needle intended to examine thepotentially-cancerous lesion does not actually contact the lesion butinstead contacts nearby healthy tissue, leading to an incorrect sampleand incorrect medical conclusion. Similarly, when attempting to removecancerous cells, two undesirable situations may arise: healthy tissuesmay be removed together with the cancerous cells, or some cancerouscells may be left unremoved.

Accordingly, in accordance with some embodiments described herein, amedical device may be used to determine the presence of a cancerouscell/tissue, the characteristics of a cancerous cell/tissue, and/or orthe type of cancerous cell/tissue (e.g., carcinomas, lymphomas,myelomas, neoplasms, melanomas, metastases or sarcomas). For example,the machine learning techniques described above may be used todifferentiate between cancerous cell/tissues and non-cancerousbiological materials and/or to characterize cancerous cell/tissues.Furthermore, techniques of the type described herein (including machinelearning techniques) may provide recommendations on how to treat acancerous cell/tissue based, at least in part, on the characteristics ofthe cancerous cell/tissue. For example, ablation or removal of acancerous cell/tissue may be recommended in some circumstances, as wellas a manner in which to ablate or remove.

Examples of medical devices, sensors, and manners of sensingtissues/materials of a cancerous cell are described in detail above withrespect to FIGS. 2-11 . Described below in connection with FIGS. 31-33are examples of techniques that may be implemented by such a medicaldevice and/or that a medical device may be operated to perform.

FIG. 31 illustrates, an exemplary process 3100 that may be performed bya medical device operating in accordance with some techniques describedherein. In the example of FIG. 31 , the sensor may be disposed indiagnosis and/or treatment devices, such as in needles, ablationcatheters, radiofrequency probes, robotic probes, laparoscopcs, orcutting devices. In some embodiments, the sensor is disposed near thedistal end of the medical device. The medical device may generatetreatment recommendations based on characteristic(s) of the cancerouscell determined using the sensor. It should be appreciated that theprocesses described herein are not limited to use with invasive probes.In some embodiments, techniques described herein may be used withsystems and devices that include non-invasive probes that may not bedesigned for use or solely for use within a body of an animal but may beadditionally or alternatively be designed for use on biologicalstructures, including tissues, on an exterior of an animal's body. Forexample, in some embodiments, devices, systems, and techniques describedherein may be used for diagnosis and/or treatment of superficiallesions, such as skin cancer or other skin conditions.

The process 3100 begins in block 3102, in which an invasive probe of amedical device is operated to detect one or more characteristics (e.g.,size and/or composition) of a lesion that is proximate to the sensor,which may be a cancerous tissue or cell. Prior to the start of theprocess 3100, the invasive probe may be inserted into the body of ananimal and moved proximate to a predicted location of the lesion. Themedical device then is operated to detect when the sensor contacts thelesion. Contact of the lesion, or of the tissue that is known to be oris potentially cancerous, may be determined by evaluating a change overtime in a value output by the sensor (e.g., a change in impedance), orusing machine learning techniques as described in connection with FIG.17C. For example, the medical device may output (e.g., to a user, via auser interface) one result when the sensors of the invasive probe arenot contacting a cancerous tissue/cell, or is not contacting the type oftissue of which the lesion is known to be a part.

For example, when the lesion to be investigated, as the invasive probeis moved through the animal toward the lesion, the medical device mayoutput a value that is indicative of a tissue that it is contacting. Thevalue may in some embodiments be a qualitative value, including a binaryvalue such as a yes/no or true/false value to indicative whether theinvasive probe is contacting the lesion.

The medical device may determine whether the invasive probe iscontacting the lesion by analyzing the biological material(s) contactedby the invasive probe, including the tissues contacted by the invasiveprobe, to determine whether the invasive probe is contacting anybiological materials that are “abnormal” and thus may be a part of alesion. The medical device may, in some embodiments, determine whether abiological material contacted by the probe is “abnormal” by evaluating alocation of the invasive probe within the animal, which may indicatebiological materials that the invasive probe may be expected to contact.

The medical device may additionally or alternatively determine whetherthe invasive probe is contacting the lesion based on predictions aboutthe lesion, which may be input by a clinician as a result of apreliminary diagnosis. For example, the clinician may input informationpreliminarily characterizing a lesion, such as whether the lesion is invasculature or is a lesion of an organ, or in the case of a lesion of anorgan what the organ is, a prediction of a composition of the lesion, ora prediction of a state of tissues or cells of the lesion (e.g.,unhealthy, inflamed, cancerous, diseased, etc.). In embodiments in whichsuch information is input, the clinician may input the informationpreliminarily characterizing the lesion individually, or may make aselection of a preliminary diagnosis of the lesion that may beassociated with such information preliminarily characterizing the lesion(e.g., by selecting a particular category of atheroma, other informationsuch as an expected composition of the atheroma and that it is locatedin vasculature may also be selected). As the invasive probe movesthrough the animal, the medical device may compare biological materialscontacted by the invasive probe to the preliminary characterization ofthe lesion to determine whether the invasive probe is contacting thelesion. For example, if the lesion has been preliminarily diagnosed as abrain lesion that may be a brain tumor, the medical device may determinewhether the invasive probe has contacted abnormal brain tissue and/orwhether the invasive probe has contacted cancerous brain tissue, andoutput this result.

In other embodiments, rather than merely providing a binary valueindicating whether the invasive probe is contacting the lesion, themedical device may output a value indicative of, for example, anidentity, quantity and/or relative abundance of a biological material orbiological materials being contacted by sensors of the invasive probe,which may vary as the probe moves through the body. The value indicativeof the material(s) may be an identification of the materials, such as alist of materials identified from impedance spectra, as determined usingtechniques described herein (including the machine learning techniquesdescribed above). The values may, in other embodiments, be numericvalues, such as values detected by sensors (e.g., an impedance value, orimpedance spectrum) or other values.

The probe and its sensor may be moved until contacting a lesion, atwhich point a result output by the medical device may change oncecontact is made. In this manner, a location of a lesion may bedetermined using the invasive probe, and a determination may be madethat the invasive probe is contacting the lesion.

The invasive probe may additionally, in some cases, be operated todetermine the geometry of a lesion. For example, the geometry of alesion potentially including cancerous tissue (e.g., a tumor) may bedetermined in some embodiments by moving the invasive probe in theproximity of the lesion and identifying when sensors of the invasiveprobe are or are not contacting the lesion. For example, if an analysisof values output by the invasive probe determines that the lesionincludes cancerous tissue, the invasive probe may be moved and adetermination made, over time, and for different sensors, of whetherindividual sensors are contacting cancerous tissue. The amount ofmovement of the invasive probe (e.g., measured using an accelerometer,as discussed above) and position of the sensors on the invasive probemay then be analyzed by the medical device to determine a geometry ofthe cancerous tissue within the animal, including one or more dimensionsof the cancerous tissue.

In some such embodiments, the medical device may determine one or moretreatment recommendations for a lesion based on the geometry of thelesion.

In one treatment protocol that may be implemented in embodiments such asthe one illustrated in FIG. 31 , ablation may be used as a first optionfor treatment of a cancerous tissue. Accordingly, in block 3104, anablative device such as a needle or a radiofrequency probe is insertedinto the animal. In some embodiments, the ablative device may include aninvasive probe including sensors of the type described herein. Theablative device may be moved until the ablative device determines thatcontact with a cancerous cell or tissue has been formed. (Though, itshould be appreciated that embodiments are not limited to operating withan ablative device that includes an invasive probe. In otherembodiments, the invasive probe is part of a separate medical device,and after positioning of the invasive probe the ablative device is moveduntil located proximate to the invasive probe and thus located proximateto the cancerous cell/tissue.)

In block 3104, following placement of the ablative device proximate tothe cancerous cell/tissues, the ablative device is operated to ablatethe cancerous cell/tissue. Following a treatment time interval, theablative device may be operated to determine whether the ablative deviceis having an effect on the cancerous cell/tissue. For example, in someembodiments, a treatment recommendation may be generated that guides aclinician in performing the ablation, including whether the ablation iseffective and whether to continue with the ablation. Accordingly, inblock 3106, the sensor may provide information indicating whether theablative device is still in contact with cancerous cells or canceroustissue. This determination may be made using techniques described herein(including the machine learning techniques described above). Theinformation may be processed and may be used to provide a treatmentrecommendation, such as whether to stop the ablation or continue theablation, or to check the positioning of the invasive probe beforedetermining whether to stop ablating.

In some embodiments, the ablative device may include multiple differentelectrodes with which to ablate, such as different electrodes positionedat different locations, and the different electrodes may be individuallyoperable, such that some may be operated to ablate at a time that othersare not being operated to ablate. In some embodiments, each ablationelectrode may be disposed near sensing electrodes, with the sensingelectrodes being operated in accordance with techniques described hereinto determine a biological material contacted by the sensing electrode.The ablative device may determine, using the sensing electrodes, whethera particular part of the ablative device is contacting canceroustissue/cell or noncancerous tissue/cell. In some such embodiments, inresponse to determining that a part of the ablative device is contactingnoncancerous tissue, the ablative device may cease or prevent operationof the ablative electrodes of that part of the ablative device, to limitablation to only the cancerous tissues and minimize damage that may bedone to noncancerous tissues.

In this way, the clinician may stop ablating if this treatment isineffective, and the clinician may only continue ablating while theablative device is in contact with the cancerous cell/tissue and therebyonly ablate cancerous tissues. The clinician may thus be more confidentat the end of the treatment of whether the treatment was successful and,if it was successful, that all of the cancerous cell/tissue has beenablated. In this way, the risk of ablating healthy tissues, and/or therisk of leaving cancerous cells un-ablated, is mitigated.

Accordingly, as illustrated in FIG. 31 , if it is determined that theablative device is still in contact with the cancerous cell/tissue,process 3100 proceeds to block 3108, in which a recommendation tocontinue to ablate is provided, and iterates to block 3104. Otherwise,if it is determined that the ablative probe is no longer in contact withthe cancerous cell/tissue, a recommendation to stop the ablation isprovided in block 3110. The process may be repeated by repositioning theablative device. If contact can no longer be formed with a lesion evenafter several attempts to reposition the ablative device, process 3100ends.

FIG. 32 illustrates an example of a manner of operating a medical deviceto generate treatment recommendations for a cancerous cell/tissue inaccordance with another embodiment. In the embodiment of FIG. 32 , amedical device may include multiple sensors arrayed along an exterior ofa probe, such as in the example of FIG. 3 discussed above. As should beappreciated from the foregoing, with such an array of sensors, severaldifferent characteristics of a cancerous lesion may be determined,including composition of the cancerous lesion. For example, byperforming an EIS process on the cancerous lesion, a composition of thecancerous lesion may be determined, as discussed above. In someembodiments, trained machine learning models as described above may beused to determine the composition or other characteristics of thecancerous lesion.

The process 3200 of FIG. 32 begins in block 3202, in which a medicaldevice is inserted into the body of an animal subject and operated todetect one of more characteristics of a cancerous lesion, for example acomposition of a cancerous lesion. Based on the characteristics,including the composition, the medical device may in block 3204 select atreatment option to recommend. Based on the composition, process 3200may determine the type of cancerous lesion being probed, and anappropriate treatment recommendation may be provided. The medical devicemay be configured, such as in other embodiments described above, withinformation on impedance spectra and other electrical characteristics(e.g., effective capacitance) for different biological materials and oncompositions of different lesions, such that biological materials may beidentified using impedance spectra and lesions may be identified basedon biological materials. The medical device may be further configuredwith different treatment recommendations for different types of lesions,such as different types of cancerous lesions. In one example, if it isdetermined using impedance spectra for different biological materials ofthe lesion that the cancerous lesion is or is a part of a carcinoma, arecommendation to remove the cancerous lesion may be provided. Inanother example, if is it determined that the cancerous lesion is partof a melanoma, radiofrequency ablation may be recommended. The medicaldevice may select the treatment option in any suitable manner.

Once a treatment is recommended in block 3204, the medical device may inblock 3206 monitor performance of the selected treatment option. Themedical device may monitor the treatment using one or more sensors, suchas the one or more sensors with which the characteristics weredetermined in block 3202 or one or more sensors of a treatment devicethat is operated to perform the treatment. For example, if ablation isrecommended in block 3204, a clinician may insert an ablative device.The ablative device may have a sensor, such as a temperature sensor, forsensing the state of the cancerous lesion as ablation is beingperformed. The sensor may detect whether the ablation was successful bydetermining whether the cancerous lesion is burned or frozen.

In block 3208, information on a status of a treatment is output by themedical device via a user interface, for presentation to a clinician.Then, the process 3200 ends.

While an example of monitoring a treatment is given in the context ofgenerating treatment recommendations, it should be appreciated thatsimilar techniques may be used to raise error messages or other messagesto a clinician regarding a status of a treatment. For example, if asensor on a treatment device indicated presence of the cancerous lesionfor a time, after which the sensor no longer detects the cancerouslesion, the medical device may determine that the treatment device isimproperly positioned or that the cancerous lesion was lost. This mayindicate either that the device needs to be repositioned or that thecancerous lesion has moved. A message to the clinician via the userinterface may indicate such a potential problem.

Additionally, while the example of FIG. 32 described a manner ofoperating a medical device to provide treatment recommendations bothrelating to an initial selection of a treatment and related to asubsequent manner of performing that treatment, it should be appreciatedfrom the foregoing that embodiments are not so limited. For example, insome embodiments, a medical device may include one or more sensors asdescribed herein and may be operated to produce treatmentrecommendations on a manner of operation of that device, withoutgenerating an initial recommendation to use that device. For example, aneedle or a radiofrequency probe, as discussed above, may include one ormore sensors to generate data on a status or performance of a treatmentand may produce treatment recommendations.

FIG. 33 illustrates a process 3300 that may be implemented by a medicaldevice in some embodiments for generating treatment recommendations.

The process 3300 begins in block 3302, in which the medical device isoperated to determine one or more characteristics (e.g., size and/orcomposition) of a cancerous lesion, using techniques described herein.The medical device may receive the characteristic(s) from a component ofthe medical device. For example, one or more sensors included in themedical device and/or another component that generates characteristic(s)based on data produced by the sensors. The characteristic(s) may includea composition of the cancerous lesion, in some embodiments. Thecharacteristic(s) may additionally or alternatively include a locationof the cancerous lesion within the body, one or more dimensions of anaggregate of cancerous lesion (e.g., a length, a thickness, etc.), atemperature of the cancerous lesion, or other information that may bedetermined based on the types of sensors described above.

In block 3304, the medical device compares the characteristic(s)received in block 3302 to one or more conditions for one or moretreatment options. The medical device may be configured with informationon multiple different available treatment options, each of which may beassociated with one or more conditions that relate to one or morecharacteristics of a cancerous lesion. The treatment options may includeablation, removal and biopsy. Examples of such conditions related to acomposition of a cancerous lesion are described above in connection withFIG. 32 . Trained machine learning models, for example as described inconnection with FIG. 15B, may be used to determine relationships betweencancerous lesion characteristics and options for successful treatments.

The medical device may compare the characteristic(s) of the cancerouslesion to the conditions for one or more treatment options to determinewhich conditions are met. In some embodiments, the sets of conditionsfor treatment options may be mutually exclusive, such that a cancerouslesion may meet only one set of conditions and thus only one treatmentoption may be selected. In other embodiments, the set of conditions maynot be mutually exclusive, and the medical device may determine whichtreatment option to recommend by identifying the one for which the mostcorresponding conditions are met or the one for which the correspondingconditions are met most closely. For example, in the case that differentconditions are associated with different ranges of values, such asranges of impedance spectra, a condition may be determined byidentifying a range for which a value for a lesion most closely matches.The closest match may be the range, for example, that the lesion'simpedance spectrum or other value falls within or is the farthest from aboundary value for the range, or has the most overlap with the range.

In block 3306, based on the comparison, the medical device may output arecommendation of a treatment option via a user interface of the medicaldevice, and the process 3300 ends.

Those skilled in the art will appreciate that there are a number of waysin which to set the conditions for treatment options that may be used inconnection with a process like process 3300 of FIG. 33 . For example,values for characteristics of a cancerous lesion to use as conditionsfor selection of treatment options may be hard-coded into a medicaldevice following at least some experimentation to determine acorrespondence between the values, types of cancerous cells/tissues, andsuccessful treatment with various treatment options. The inventors haverecognized and appreciated, however, the advantages of a system to learnsuch relationships and conditions based on characteristics of cancerouscells/tissues and information on successful treatments of cancerouscells/tissues, among other information. For example, a machine learningprocess, such as one that may include feature extraction and/orclassification, may be implemented in some embodiments.

Computer Implementation

Techniques operating according to the principles described herein may beimplemented in any suitable manner. Included in the discussion above area series of flow charts showing the steps and acts of various processesthat characterize a lesion of a duct and/or generate one or moretreatment recommendations for treatment of the lesion. The processingand decision blocks of the flow charts above represent steps and actsthat may be included in algorithms that carry out these variousprocesses. Algorithms derived from these processes may be implemented assoftware integrated with and directing the operation of one or moresingle- or multi-purpose processors, may be implemented asfunctionally-equivalent circuits such as a Digital Signal Processing(DSP) circuit or an Application-Specific Integrated Circuit (ASIC), ormay be implemented in any other suitable manner. It should beappreciated that the flow charts included herein do not depict thesyntax or operation of any particular circuit or of any particularprogramming language or type of programming language. Rather, the flowcharts illustrate the functional information one skilled in the art mayuse to fabricate circuits or to implement computer software algorithmsto perform the processing of a particular apparatus carrying out thetypes of techniques described herein. It should also be appreciatedthat, unless otherwise indicated herein, the particular sequence ofsteps and/or acts described in each flow chart is merely illustrative ofthe algorithms that may be implemented and can be varied inimplementations and embodiments of the principles described herein.

Accordingly, in some embodiments, the techniques described herein may beembodied in computer-executable instructions implemented as software,including as application software, system software, firmware,middleware, embedded code, or any other suitable type of computer code.Such computer-executable instructions may be written using any of anumber of suitable programming languages and/or programming or scriptingtools, and also may be compiled as executable machine language code orintermediate code that is executed on a framework or virtual machine.

When techniques described herein are embodied as computer-executableinstructions, these computer-executable instructions may be implementedin any suitable manner, including as a number of functional facilities,each providing one or more operations to complete execution ofalgorithms operating according to these techniques. A “functionalfacility,” however instantiated, is a structural component of a computersystem that, when integrated with and executed by one or more computers,causes the one or more computers to perform a specific operational role.A functional facility may be a portion of or an entire software element.For example, a functional facility may be implemented as a function of aprocess, or as a discrete process, or as any other suitable unit ofprocessing. If techniques described herein are implemented as multiplefunctional facilities, each functional facility may be implemented inits own way; all need not be implemented the same way. Additionally,these functional facilities may be executed in parallel and/or serially,as appropriate, and may pass information between one another using ashared memory on the computer(s) on which they are executing, using amessage passing protocol, or in any other suitable way.

Generally, functional facilities include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionalityof the functional facilities may be combined or distributed as desiredin the systems in which they operate. In some implementations, one ormore functional facilities carrying out techniques herein may togetherform a complete software package. These functional facilities may, inalternative embodiments, be adapted to interact with other, unrelatedfunctional facilities and/or processes, to implement a software programapplication.

Some exemplary functional facilities have been described herein forcarrying out one or more tasks. It should be appreciated, though, thatthe functional facilities and division of tasks described is merelyillustrative of the type of functional facilities that may implement theexemplary techniques described herein, and that embodiments are notlimited to being implemented in any specific number, division, or typeof functional facilities. In some implementations, all functionality maybe implemented in a single functional facility. It should also beappreciated that, in some implementations, some of the functionalfacilities described herein may be implemented together with orseparately from others (i.e., as a single unit or separate units), orsome of these functional facilities may not be implemented.

Computer-executable instructions implementing the techniques describedherein (when implemented as one or more functional facilities or in anyother manner) may, in some embodiments, be encoded on one or morecomputer-readable media to provide functionality to the media.Computer-readable media include magnetic media such as a hard diskdrive, optical media such as a Compact Disk (CD) or a Digital VersatileDisk (DVD), Blu-Ray disk, a persistent or non-persistent solid-statememory (e.g., Flash memory, Magnetic RAM, etc.), or any other suitablestorage media. Such a computer-readable medium may be implemented in anysuitable manner, including as computer-readable storage media 3406 ofFIG. 34 described below (i.e., as a portion of a computing device 3400)or as a stand-alone, separate storage medium. As used herein,“computer-readable media” (also called “computer-readable storagemedia”) refers to tangible storage media. Tangible storage media arenon-transitory and have at least one physical, structural component. Ina “computer-readable medium,” as used herein, at least one physical,structural component has at least one physical property that may bealtered in some way during a process of creating the medium withembedded information, a process of recording information thereon, or anyother process of encoding the medium with information. For example, amagnetization state of a portion of a physical structure of acomputer-readable medium may be altered during a recording process.

In some, but not all, implementations in which the techniques may beembodied as computer-executable instructions, these instructions may beexecuted on one or more suitable computing device(s) operating in anysuitable computer system, or one or more computing devices (or one ormore processors of one or more computing devices) may be programmed toexecute the computer-executable instructions. A computing device orprocessor may be programmed to execute instructions when theinstructions are stored in a manner accessible to the computing deviceor processor, such as in a data store (e.g., an on-chip cache orinstruction register, a computer-readable storage medium accessible viaa bus, etc.). Functional facilities comprising these computer-executableinstructions may be integrated with and direct the operation of a singlemulti-purpose programmable digital computing device, a coordinatedsystem of two or more multi-purpose computing device sharing processingpower and jointly carrying out the techniques described herein, a singlecomputing device or coordinated system of computing device (co-locatedor geographically distributed) dedicated to executing the techniquesdescribed herein, one or more Field-Programmable Gate Arrays (FPGAs) forcarrying out the techniques described herein, or any other suitablesystem.

FIG. 34 illustrates one exemplary implementation of a computing devicein the form of a computing device 3400 that may be used in a systemimplementing techniques described herein, although others are possible.It should be appreciated that FIG. 34 is intended neither to be adepiction of necessary components for a computing device to operate inaccordance with the principles described herein, nor a comprehensivedepiction.

Computing device 3400 may comprise at least one processor 3402, anetwork adapter 3404, and computer-readable storage media 3410.Computing device 3400 may be, for example, a medical device as describedabove, a desktop or laptop personal computer, a personal digitalassistant (PDA), a smart mobile phone, a server, or any other suitablecomputing device. Network adapter 3404 may be any suitable hardwareand/or software to enable the computing device 3400 to communicate wiredand/or wirelessly with any other suitable computing device over anysuitable computing network. The computing network may include wirelessaccess points, switches, routers, gateways, and/or other networkingequipment as well as any suitable wired and/or wireless communicationmedium or media for exchanging data between two or more computers,including the Internet. Computer-readable media 3410 may be adapted tostore data to be processed and/or instructions to be executed byprocessor 3402. Processor 3402 enables processing of data and executionof instructions. The data and instructions may be stored on thecomputer-readable storage media 3410.

In embodiments in which the device 3400 is a medical device as describedherein, the device 3400 may include an invasive medical device 3406 thatis to be inserted into anatomy of a subject to diagnose and/or treat thesubject. The device 3406 includes an invasive probe 3408, as discussedabove.

The data and instructions stored on computer-readable storage media 3410may comprise computer-executable instructions implementing techniqueswhich operate according to the principles described herein. In theexample of FIG. 34 , computer-readable storage media 3410 storescomputer-executable instructions implementing various facilities andstoring various information as described above. Computer-readablestorage media 3410 may store a lesion analysis facility 3412 to analyzeone or more characteristics of a lesion, including a composition of alesion, and/or to determine a treatment recommendation based on theanalysis. The computer-readable storage media 3410 may additionallystore conditions 3414 for treatment options that may be used by thefacility 3412. The computer-readable storage media 3410 may also store alearning facility 3416 and a chronicle generation facility 3418.

While not illustrated in FIG. 34 , a computing device may additionallyhave one or more components and peripherals, including input and outputdevices. These devices can be used, among other things, to present auser interface. Examples of output devices that can be used to provide auser interface include printers or display screens for visualpresentation of output and speakers or other sound generating devicesfor audible presentation of output. Examples of input devices that canbe used for a user interface include keyboards, and pointing devices,such as mice, touch pads, and digitizing tablets.

As another example, a computing device may receive input informationthrough speech recognition or in other audible format.

Embodiments have been described where the techniques are implemented incircuitry and/or computer-executable instructions. It should beappreciated that some embodiments may be in the form of a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Various aspects of the embodiments described above may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. Any embodiment, implementation, process,feature, etc. described herein as exemplary should therefore beunderstood to be an illustrative example and should not be understood tobe a preferred or advantageous example unless otherwise indicated.

Having thus described several aspects of at least one embodiment, it isto be appreciated that various alterations, modifications, andimprovements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the spirit and scope ofthe principles described herein. Accordingly, the foregoing descriptionand drawings are by way of example only.

1.-60. (canceled)
 61. An apparatus comprising: at least one medicaldevice at least one impedance sensor to obtain a plurality of sets ofimpedance measurements of a biological structure in vivo, each set ofthe plurality of sets of impedance measurements comprising measurementsat a set of frequencies, the set of frequencies comprising a firstfrequency and a second frequency; and at least one control circuitconfigured to process information regarding the biological structureusing at least one trained model trained to distinguish betweenbiological structures having different characteristics, wherein theinformation regarding the biological structure processed using the atleast one trained model comprises the plurality of sets of impedancemeasurements.